# Data Science Data science is a field that combines domain expertise, programming, mathematics, and statistics to extract meaningful insights from data. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Naoki Egami's Research on Political Methodology and External Validity DevFeed: [Naoki Egami's Research on Political Methodology and External Validity]() Original publisher: [Read original article]() Author: Peter Dizikes | MIT News Published: 2026-09-16T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [AI Research]() Tags: [artificial-intelligence](), [external-validity](), [faculty](), [idss](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [naoki-egami](), [political-methodology](), [political-science](), [profile](), [research](), [school-of-humanities-arts-and-social-sciences](), [science](), [social-sciences](), [statistics](), [technology-and-society](), [voting-and-elections]() ### AI overview An MIT profile of political scientist Naoki Egami, whose research examines research methodology, external validity, and the mathematical and statistical challenges of studying civic and political phenomena. It also discusses his work on the use of AI tools in research. ### Source excerpt Naoki Egami has become a standout in political methodology, helping refine tools that give scholars durable results. ## Building an AI-native data & insights operating system at Webflow DevFeed: [Building an AI-native data & insights operating system at Webflow]() Original publisher: [Read original article]() Author: Ashwini Chaube Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [data](), [Artificial Intelligence](), [Platform Engineering](), [Data Science](), [decision-making](), [Authorization]() Tags: [agentic](), [ai](), [data](), [data-insights](), [inside-webflow](), [permissions](), [review](), [self-service](), [skills]() ### AI overview Webflow describes how its Data & Insights team built an AI-native operating system for trusted self-service analytics. The approach combines governed data, encoded business context, reusable skills and agents, permissions, architectural controls, review practices, and human judgment, while also changing how the team works through agent-first workflows, learning, and experimentation. ### Source excerpt How we built the governed foundations for trusted self-service analytics while transforming the way our own team works. ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## Why Spotify Is Not Using Bayesian A/B Testing DevFeed: [Why Spotify Is Not Using Bayesian A/B Testing]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-09-08T13:18:44Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [Data Science](), [Tooling]() Tags: [a-b-testing](), [data](), [data-science](), [experimentation](), [inference](), [statistics](), [testing](), [tooling]() ### AI overview Spotify argues that Bayesian A/B testing can be oversimplified and that its claimed advantages require careful configuration. The article compares Bayesian and frequentist approaches and explains why Spotify does not currently need Bayesian inference alongside its frequentist tooling. ### Source excerpt Clearing the confusion about what Bayesian A/B testing is. The post Why Spotify Is Not Using Bayesian A/B Testing appeared first on Spotify Engineering. ## How an MIT research project became a global programming language DevFeed: [How an MIT research project became a global programming language]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-31T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [The Julia Language](), [Programming language](), [Data Science](), [Complex Systems](), [Data analysis](), [Open Source]() Tags: [ai](), [ai-platform](), [alan-edelman](), [analysis](), [applications](), [artificial-intelligence](), [chris-rackauckas](), [complex-systems](), [computer-science-and-artificial-intelligence-laboratory-csail](), [deshpande-center](), [jeff-bezanson](), [julia-programming-language](), [juliahub](), [language](), [mit-schwarzman-college-of-computing](), [open-source](), [programming](), [programming-language](), [software](), [startups](), [stefan-karpinski](), [viral-shah]() ### AI overview An MIT research project created Julia, a free and open-source programming language for scientific research, data analysis, and complex-systems modeling. The article describes Julia's adoption by researchers, engineers, companies, and universities, and introduces JuliaHub's Dyad 3.0 AI platform. ### Source excerpt With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more. ## The Real Python Podcast - Episode #309: Exploring Complex Systems & Maintainable Data Science Pipelines DevFeed: [The Real Python Podcast - Episode #309: Exploring Complex Systems & Maintainable Data Science Pipelines]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-28T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Complex Systems](), [Data Science](), [Python](), [systems](), [configuration](), [ci](), [YAML](), [releases](), [Django](), [VS Code Extension]() Tags: [code](), [complex-systems](), [configuration](), [data-science](), [django](), [podcast](), [python](), [release](), [systems](), [vs-code](), [workflow]() ### AI overview This Real Python podcast episode explores complex versus complicated coding problems and practical patterns for designing maintainable systems. It also covers repeatable data science pipelines, DataFrame validation with Pointblank, configuration-driven modular workflows, Python releases, PEPs, Django's release cycle, and several Python community projects. ### Source excerpt What are the key characteristics of complex systems, and what are practical patterns for tackling complex coding problems? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects. ## LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs' Probabilistic Beliefs DevFeed: [LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs' Probabilistic Beliefs]() Original publisher: [Read original article]() Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [Uncertainty quantification LLMs](), [AI Inference](), [Data Science]() Tags: [ai](), [data-science](), [diagnostics](), [llms](), [research]() ### AI overview This research examines whether large language models update probabilistic beliefs in accordance with Bayes' rule. It introduces the information processing gap to quantify deviations from Bayesian updates, compares evidence-integration approaches, and finds that heuristic, non-Bayesian updates can outperform exact Bayesian updates on downstream tasks. ### Source excerpt Modern AI systems are being deployed in complex domains such as medicine, science, and law, where there is often not a single correct answer given the observed evidence. Such systems must be able to represent and update uncertain beliefs about the world as new evidence arrives to make rational decisions. We introduce the novel technique of studying LLMs as information processing rules and utilize the information processing gap--the deviation from Bayes updates--to study the internal (in)consistencies of how LLMs update their probabilistic beliefs from evidence. Our extensive experiments evaluate... ## How we knew COVID was over (and what our models had to unlearn) DevFeed: [How we knew COVID was over (and what our models had to unlearn)]() Original publisher: [Read original article]() Author: Harrison Katz Published: 2026-08-19T17:01:03Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Data Science](), [data](), [datasets](), [Process]() Tags: [company](), [data](), [data-modeling](), [data-science](), [machine-learning](), [models](), [process](), [technology]() ### AI overview An Airbnb forecasting data science team explains how it responds when production forecasts drift, distinguishing between refitting a model with newer data, respecifying its structure, and holding it unchanged. The article emphasizes diagnosing the source of persistent bias and managing the risks of model updates that influence company decisions. ### Source excerpt When we retrain, when we rebuild, and when we leave a model alone. By: Harrison Katz A forecast that carries weight The Forecasting Data Science team at Airbnb produces many of the forecasts the rest of the company plans around: demand, bookings, cancellations, and a range of finer cuts by market and segment, refreshed continuously across thousands of markets. The targets differ, and the models differ, but they have one thing in common: Other teams build on top of them. This means a forecast that is casually wrong is not a clean miss, as it might be in an academic setting. That's because a small bias does not stay small once a lot of decisions are riding on it. So when one of those forecasts starts to drift, what to do about it is not really a methods question. It is a risk question, and an easy one to get wrong, which we have from time to time. One of these forecasts had been missing, compared to what actually happened after the forecast was released, in the same direction for a couple of quarters. This bias persisted after several routine refreshes. The usual solution would be to fully retrain the model: pull in the recent data, refit the model again, and ship. But we wanted to understand the source of the bias, rather than simply hoping an update would eliminate it. If you're interested in other posts on this topic, you can learn more about how COVID impacted Airbnb's financial models or how we dealt with disruption to our models during the pandemic. This post is about the discipline that came out of both: how we now decide whether a struggling forecast needs new data, a new model, or no changes at all. One word, three decisions The easy mistake is treating the choice to "retrain" a model as a single action. It is three separate actions -- refitting, respecifying, or holding -- and none of them is particularly similar to the others. Refitting is the cheaper option. Same model, same structure, same features, updated with newer data. This is what most people mean by ## Two ways to measure the cumulative impact of experiments DevFeed: [Two ways to measure the cumulative impact of experiments]() Original publisher: [Read original article]() Author: Lukas Goetz-Weiss; Eddie Cai Published: 2026-08-18T00:00:00Z Content type: opinion Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments](), [Statistics](), [Data Science]() Tags: [analysis](), [experimentation](), [experiments](), [product](), [statistics](), [testing]() ### AI overview This article explains why summing the observed lift from winning experiments overstates cumulative impact because of the winner's curse. It presents two established approaches: a randomized holdout that measures the combined effect directly, and a statistical correction that aggregates existing experiment estimates. Datadog's Cumulative Impact feature applies the correction without requiring a quarter-long holdout and can analyze an entire experimentation program or a filtered team subset. ### Source excerpt Summing individual wins overstates true impact. See two accurate methods, holdouts and Datadog's Cumulative Impact, and how to choose between them. ## Measuring time savings from Figma Make DevFeed: [Measuring time savings from Figma Make]() Original publisher: [Read original article]() Author: Remy Stewart Published: 2026-08-11T19:59:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Data Science](), [Artificial Intelligence](), [A/B Testing]() Tags: [a-b-testing](), [ai](), [ai-tools](), [data-science](), [figma](), [productivity](), [research](), [testing]() ### AI overview Figma's Data Science team describes a randomized controlled trial measuring how Figma Make affects users' design work. Across 100 participants, design work was 20% faster and 16% easier overall, while PMs saw tasks become 23% faster and 37% easier. The article explains why confounders make AI time savings difficult to measure and discusses the limitations of online A/B testing and causal inference using log data. ### Source excerpt The Figma Data Science team assumed that AI saves users time--but quantifying it required a new approach to research design. ## Alexander Rakhlin named director of the MIT Statistics and Data Science Center DevFeed: [Alexander Rakhlin named director of the MIT Statistics and Data Science Center]() Original publisher: [Read original article]() Author: Institute for Data, Systems, and Society Published: 2026-08-03T19:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [Machine learning]() Tags: [ai-in-statistics](), [alexander-sasha-rakhlin](), [alumni-ae](), [ankur-moitra](), [artificial-intelligence](), [brain-and-cognitive-sciences](), [computer-science-and-technology](), [data-science](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [fotini-christia](), [idss](), [laboratory-for-information-and-decision-systems-lids](), [leadership](), [lids](), [machine-learning](), [mit-bcs](), [mit-faculty-appointments](), [mit-idss](), [mit-leadership](), [mit-schwarzman-college-of-computing](), [mit-statistics-and-data-science-center-sdsc](), [philippe-rigollet](), [richard-dick-larson](), [school-of-engineering](), [school-of-science](), [statistics]() ### AI overview Alexander Rakhlin has been named the next director of MIT's Statistics and Data Science Center, succeeding Ankur Moitra. Rakhlin has been affiliated with the center since 2016 and has led its interdisciplinary doctoral program, overseeing more than 75 successful PhD defenses. ### Source excerpt An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra. ## Accelerating scientific discovery with ChatGPT for Academic Researchers DevFeed: [Accelerating scientific discovery with ChatGPT for Academic Researchers]() Original publisher: [Read original article]() Published: 2026-07-29T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [AI Models](), [OpenAI](), [Data Science](), [Security]() Tags: [ai](), [ai-models](), [applications](), [chatgpt](), [collaboration](), [company](), [engineering](), [frontier-ai](), [models](), [openai](), [privacy](), [productivity](), [research](), [security](), [tools](), [training]() ### AI overview OpenAI is introducing ChatGPT for Academic Researchers, offering selected academic institutions free access to frontier models and research tools. The program aims to support 100,000 scientists, mathematicians, and engineers with scientific discovery, collaboration, productivity, training, and hands-on support, while providing business-grade privacy and security protections. ### Source excerpt OpenAI is giving 100,000 academic researchers free access to ChatGPT's most advanced AI models to accelerate scientific research, collaboration, and discovery. ## Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest DevFeed: [Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-07-27T16:01:02Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [recommendation systems](), [Data Science](), [data]() Tags: [engineering](), [growth](), [interest-exploration](), [metrics](), [personalization](), [pinterest](), [recommendation-system](), [retention](), [retrieval](), [understanding-user]() ### AI overview Pinterest introduces Pinner Progression, a recommendation-system program that uses persistent User Interest Clusters to improve discovery and make retention a first-class objective alongside engagement. ### Source excerpt Part 1 of 2 Authors Personalization (Homefeed): Yuke Yan, Chuxi Wang, Andreanne Lemay, Olafur Gudmundsson, Anna Kiyantseva, Krystal Benitez, Jongho Kim, Jiacong He, Rahul Goutam, James Li, Dylan Wang User Understanding: Simin Li, Sufyan Suliman, Yingjian Ding, Hongbo Deng Data Science: Armando Ordorica, Yan Chen, Ellie Zhang, Karim Wahba Introduction Pinterest's mission is to help people discover the inspiration to create a life they love. Our recommendation system serves hundreds of millions of users, surfacing billions of Pins across interests ranging from home renovation to meal planning to wedding decor. The home feed , where much of that discovery happens, is powered by a multi-stage pipeline spanning retrieval, lightweight scoring, ranking, and re-ranking [1][2][3]. Most of our prior work on this pipeline has been optimized for engagement: clicks, saves, downloads, closeups. These are strong signals of immediate relevance, and optimizing for them has driven significant gains across the system [4][5]. The problem is that engagement and retention are different things. A user can save ten sourdough recipes today and churn next month anyway. All we did was feed them more of what they already liked: we never helped them find something new for next time. This post is one of two that introduces Pinner Progression, a program that reframes the home feed recommendation system around retention as a first-class objective. Our core insight is that by augmenting sequential, action-by-action user understanding with holistic, persistent use-case representation, we can reliably anticipate the user's next moves and start to serve recommendations that ignite their serendipitous discovery. In this post, we introduce the key use-case representation signal: User Interest Clusters (UICs) -- and describe its construction, integration into the recommendation stack, and impact on engagement on retention metrics. A follow-up will cover how we predict unseen UICs and conduct systematic us ## No Dumb Questions: What is the AI bottleneck? How does context engineering fix it? DevFeed: [No Dumb Questions: What is the AI bottleneck? How does context engineering fix it?]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-24T16:00:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Data Science]() Tags: [agentic-ai](), [ai](), [ai-adoption](), [autonomous-agents](), [data-science](), [no-dumb-questions]() ### AI overview Stack Overflow's No Dumb Questions series explores an AI adoption bottleneck: AI tools can perform tasks but often lack the surrounding context from email threads, Slack conversations, and meetings. Michael Foree explains how context engineering can help people provide relevant information so AI produces more useful responses. ### Source excerpt In this No Dumb Questions, Stack's Director of Data Science Michael Foree teaches Phoebe about AI context, context engineering, and what she can do to become a better context engineer. ## Personalizing Airbnb search by learning from the guest journey DevFeed: [Personalizing Airbnb search by learning from the guest journey]() Original publisher: [Read original article]() Author: Daochen Zha Published: 2026-07-21T17:01:04Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Transformer](), [recommendation systems](), [Temporal data](), [Data Science]() Tags: [ai](), [blog](), [conversion](), [engineering](), [machine-learning](), [model](), [recommendation-system](), [research](), [scale](), [search](), [technology](), [training]() ### AI overview Airbnb describes a Transformer-based sequence model for personalizing search by encoding years of guest behavior, including listing views, bookings, reviews, and cancellations. The system learns richer representations of guest preferences to improve listing relevance and booking conversion while addressing very long, noisy event sequences and the cost of training on hundreds of millions of search-label pairs. ### Source excerpt How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Jun Shi, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Michael Kinoti, Stephanie Moyerman, and Sanjeev Katariya Introduction Planning a trip on Airbnb rarely happens in a single session. A guest searching for a place to stay in San Francisco might browse dozens of listings over several days, leaving behind a trail of views. Typically, over a period of years, that same guest will have accumulated many previous bookings, reviews, and the occasional cancellation. Taken together, these events reveal a great deal about what that guest values in a stay. For years, Airbnb's search ranking captured this through hand-crafted features: aggregated statistics such as total past bookings or average listing price. These worked well, but as the feature count grew into the hundreds, the approach became harder to scale and increasingly limited in expressiveness. In this blog post, we describe how we built a sequence modeling system that encodes the full guest journey using a Transformer, learning richer representations of guest preferences to deliver more personalized search results. An example of a guest journey, which is typically long, exploratory, and complex.Challenges Event sequences per guest present three core challenges. First, they are dominated by listing views, which account for the vast majority of all events -- some guests accumulate hundreds of thousands of them -- making raw sequences computationally intractable to model directly. The distribution of event types, with the majority being listing views. Second, unlike social media platforms, which optimize for engagement, Airbnb optimizes for booking conversion. Bookings are rare, compared to events, and deliberate, whereas a listing view could reflect genuine intent or simply idle browsing. Building a model that generalizes ## Data science workflows with ChatGPT Work DevFeed: [Data science workflows with ChatGPT Work]() Original publisher: [Read original article]() Published: 2026-07-14T00:00:00Z Content type: tutorial Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Data Science](), [dashboards](), [data](), [codex]() Tags: [analysis](), [chatgpt](), [codex](), [dashboards](), [data](), [openai-academy](), [work](), [workflows]() ### AI overview A webinar explaining how data science teams use ChatGPT Work to turn dashboards, metric definitions, exports, experiment notes, and business context into review-ready analysis assets. ### Source excerpt Learn practical ChatGPT Work workflows for root-cause briefs, KPI memos, scoped analyses, and dashboard specifications. ## One Driver, One Format, Every Language: ADBC DevFeed: [One Driver, One Format, Every Language: ADBC]() Original publisher: [Read original article]() Author: Luke Gannon Published: 2026-07-10T15:00:55Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Databases](), [data](), [Data Science](), [R](), [Ruby](), [Go Language](), [JavaScript](), [Python]() Tags: [apache](), [api](), [c](), [clickhouse](), [data](), [data-infrastructure](), [data-science](), [database](), [databases](), [go](), [java](), [javascript](), [python](), [ruby]() ### AI overview ClickHouse has introduced an official ADBC driver, providing Arrow-native, zero-conversion access to ClickHouse across multiple programming languages. Built with Rust and distributed through the ADBC Driver Foundry, it gives languages such as Ruby, R, and C standardized database access without separate ClickHouse drivers. ### Source excerpt ClickHouse now has an official ADBC driver, giving Ruby, R, C, and every other ADBC-aware tool zero-conversion, Arrow-native access to ClickHouse without a dedicated client for each language. ## A Career Journey from Cryptanalysis Research to SRE and Chief Editor at Microsoft DevFeed: [A Career Journey from Cryptanalysis Research to SRE and Chief Editor at Microsoft]() Original publisher: [Read original article]() Author: Alexandra Savelieva Published: 2026-07-07T07:16:01Z Content type: opinion Language: en Sources: [Data Science at Microsoft]() Topics: [Data Science](), [Microsoft](), [Cryptography](), [site-reliability-engineering](), [Algorithms, Complexity](), [audit trail]() Tags: [ai](), [algorithms](), [audit-trail](), [careers](), [crack](), [data-science](), [logs](), [machine-learning](), [microsoft](), [paper](), [sre]() ### AI overview The author reflects on becoming chief editor of Microsoft's Data Science + AI online publication and recounts a career spanning cryptanalysis research, Bing Ads R&D, and site reliability engineering. ### Source excerpt Thoughts on taking the helm of DS@MImage by the author (generated with ChatGPT). This week marks an important chapter of Data Science + AI at Microsoft, as well as in my own professional journey, as I step into the role of chief editor of this online publication after the farewell of its wonderful founder, Casey Doyle. This change is not something that I had planned -- rather it's a combination of unexpected circumstances have come together to make it happen, like many other things that have shaped my career and enabled this opportunity. If you read Casey's farewell article from last week, you may see why a metaphor of navigating the ocean came to mind when I was thinking about what's next for DS@M now that I'm "captaining the ship." The first chapter of my journey took place in 2010 as a Ph.D. intern in Microsoft Research. Under the supervision of Dmitry Khovratovich, I studied block hash functions. The paper that I coauthored ended up making a big splash in cryptanalysis (see Biclique attack -- Wikipedia), and a fun fact is that it took two years and several rejections at conferences and workshops for it to be recognized. Our approach involved surprisingly simple math and deterministic algorithms to crack a problem that was previously considered a "puzzle" requiring some craft with a bit of luck to solve. This was my main takeaway from the internship: twist and dissect the complex problems until they get reduced to an intuitively understood form, so that solving them becomes a matter of applying the right calculus. I returned in October 2012 as a full-time employee in Bing Ads R&D. I was expecting an applied research job and ended up as an SRE (Site Reliability Engineer) in the Audit Trail service working with logs collected for customer ads. It was "type 2" fun work -- absolutely not fun in the moment, but exciting when I look back. It served as a practical crash course that left the "bible" of SRE imprinted in my brain: how to design services for reliability, how t ## How Nubank uses causality, machine learning and Python to support credit limit increase decisions DevFeed: [How Nubank uses causality, machine learning and Python to support credit limit increase decisions]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-07-01T16:16:31Z Content type: article Language: en Sources: [Nubank]() Topics: [Data Science](), [Machine Learning & Artificial Intelligence](), [Python](), [risk-management](), [Monitoring](), [Optimization]() Tags: [causality](), [data-science](), [life-at-nu](), [machine-learning](), [monitoring](), [optimization](), [python](), [risk](), [scalability]() ### AI overview A high-level technical overview of how Nubank applies data science, predictive modeling, causality, optimization, and monitoring to support credit limit increase decisions. It discusses balancing customer experience, risk management, operational sustainability, interpretability, computational cost, and scalability. ### Source excerpt A technical, high-level view of how data science helps build more responsible and scalable credit decisions The post How Nubank uses causality, machine learning and Python to support credit limit increase decisions appeared first on Building Nubank. ## From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer DevFeed: [From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-01T13:43:11Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [Artificial Intelligence](), [cobol](), [Data Science](), [mainframes](), [Software Engineering](), [AI Strategy](), [self-service](), [dashboards](), [jupyter notebooks]() Tags: [adoption](), [ai](), [business-intelligence](), [cobol](), [data-science](), [enterprise](), [jupyter-notebooks](), [mainframes](), [programming](), [self-service](), [semantic]() ### AI overview A podcast conversation with Dave Langer about nearly three decades spanning COBOL, enterprise architecture, business intelligence, analytics, data science, machine learning, and AI. It discusses persistent data-industry problems, self-service analytics, dimensional modeling, AI adoption, semantic layers, governance, and career advice for data professionals. ### Source excerpt What happens when someone who started programming on a Commodore 64 watches AI reshape the entire data industry? ## Reflections on the Evolution of Data Science + AI at Microsoft and a Career Transition DevFeed: [Reflections on the Evolution of Data Science + AI at Microsoft and a Career Transition]() Original publisher: [Read original article]() Author: Casey Doyle Published: 2026-06-30T07:16:00Z Content type: opinion Language: en Sources: [Data Science at Microsoft]() Topics: [Data Science](), [Microsoft](), [Artificial Intelligence](), [Data visualization]() Tags: [ai](), [data-science](), [data-visualization](), [development](), [journey](), [microsoft](), [reflections](), [ship-of-theseus]() ### AI overview The author reflects on the evolution of Microsoft's Data Science + AI publication, their departure from Microsoft, and the career experiences that shaped their approach to communicating complex ideas for business impact. ### Source excerpt Reflections on storytelling, change, and what enduresPhoto by Joseph Barrientos on Unsplash. And so the mythical hero Theseus sailed home to Athens after defeating the Minotaur. To honor him, the Athenians preserved his wooden ship in their harbor down the centuries. As the old planks rotted, builders replaced them with identical new ones. Eventually, they replaced every single original piece of wood. This sparked a famous debate among philosophers: With every part replaced, is it still the same ship? Like the ship of Theseus, what endures also undergoes many changes. That idea applies not only to the Data Science + AI at Microsoft online publication you're reading now, but also to my own career journey. After two stints totaling more than 32 years at Microsoft, and more than six years leading Data Science + AI at Microsoft, I am turning the page on both chapters this month as I prepare to depart. As I mark this transition, I feel many emotions, but one that stands out most is gratitude -- for the people, opportunities, and experiences that have shaped both the work and me. Leading Data Science + AI at Microsoft (which we call DS@M internally) since its inception has been a highlight of my working life. From its initial focus on data science in early 2020 to its evolution to encompass AI starting in 2023, DS@M continues to attract an audience as it approaches 10,000 followers. For me, this is especially remarkable given some of the internal pushback that came in the time before we launched, reflecting skepticism that it was possible, much less advisable, to share our data science expertise outside the company without crossing proprietary lines. That we moved forward -- and ultimately succeeded -- was possible only because of the many authors and collaborators who believed in the idea and were willing to contribute their work. More than 340 articles later, we've amply proved we could do it, and do it well. In many ways, this work reflects the culmination of the paths th ## In 2026 The Data Fundamentals Matter More Than Ever DevFeed: [In 2026 The Data Fundamentals Matter More Than Ever]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-06-13T22:51:35Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data-engineering](), [data-modeling](), [Data Science](), [Python](), [SQL](), [Artificial Intelligence]() Tags: [2026](), [data](), [data-engineering](), [data-science](), [developer](), [fundamentals](), [python](), [sql]() ### AI overview This opinion article argues that data fundamentals remain important in 2026 despite changing technology trends and job titles. It identifies messy data and weak data foundations as persistent bottlenecks, and emphasizes SQL, Python, data modeling, and related engineering skills. ### Source excerpt Otherwise we are headed towards a massive data mess ## How AI Changes the Role of Applied Scientists DevFeed: [How AI Changes the Role of Applied Scientists]() Original publisher: [Read original article]() Author: Tilman Drerup Published: 2026-05-22T17:40:32Z Content type: article Language: en Sources: [Instacart]() Topics: [Artificial Intelligence](), [Data Science](), [AI, ML & Data Engineering]() Tags: [ai](), [analysis](), [applied-science](), [artificial-intelligence](), [changes](), [coding](), [economics](), [machine-learning](), [math](), [statistics]() ### AI overview Instacart's Economics Team examines how artificial intelligence is changing the work of applied scientists. The article frames the role as a bundle of tasks and proposes analyzing changes in the team's project portfolio from 2023 onward, with potentially larger effects on coding than on causal inference. ### Source excerpt Levi Boxell, Tilman Drerup, Alexandr Lenk The Economics Team at Instacart is an applied science team that operates at the intersection of machine learning engineering and economics. Similar to other applied science teams, our work involves a good chunk of engineering, steeped in statistics, math, theory, and strategy. And while that is still at the heart of what we do today, the surprisingly rapid emergence of artificial intelligence has also fundamentally altered our work in ways that we did not see coming. With this post, we want to provide a brief check-in and share an analysis of the patterns we are seeing from a distinctly economic perspective. To do so, we analyze the empirical dynamics of our project portfolio between 2023 and today, looking at the evolution of both the nature and quantity of our work over time. To start, let's have a quick refresher of what economists at Instacart do and provide a theoretical framework to think about the impact of technological change through AI. Background & Theoretical Framework At Instacart, economists spend their day-to-day on a diverse portfolio of tasks and activities. Similar to other applied science teams within the company, our work relies on a blend of skills, including economics, statistics, math, machine learning, data manipulation, coding, and AI. Due to this versatility in tasks, the team's work provides a particularly rich testing ground for predictions derived from economic theories concerning the impact of technological change. But what does economic theory actually tell us? A useful theoretical abstraction for an applied scientist's role is to frame it as a bundle of tasks (Autor, Levy, and Murnane, 2003), with each task characterized by its own production function (Acemoglu and Autor, 2011). Slightly simplified, a production function tells us how much output we can produce for a given level of input in a specific task. Comparisons of production functions across tasks in turn determine how we allocate our t ## The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces DevFeed: [The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces]() Original publisher: [Read original article]() Author: Lin Jia Published: 2026-05-22T08:05:43Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [A/B Testing](), [doubleml](), [Data Science](), [Tool]() Tags: [a-b-testing](), [causal-inference](), [causal-machine-learning](), [causality](), [data-science](), [doubleml](), [e-commerce](), [experimentation](), [featured](), [measuring]() ### AI overview This article explains why standard A/B tests can mismeasure features that require voluntary adoption in digital marketplaces. It presents Randomized Encouragement Design combined with Double Machine Learning as a way to estimate both causal impact among adopters and overall rollout impact. ### Source excerpt by Lin Jia, Kexin Fei The Content of this post has been presented at Pydata Amsterdam 2026 and the slides can be found here TL; DR Whenever a feature requires voluntary adoption, standard A/B testing breaks: low adoption flattens the topline, and self-selection makes adopters incomparable to non-adopters. Combining Randomized Encouragement Design (RED) with Double Machine Learning (DoubleML) recovers two answers -- the causal lift for adopters and the rollout's overall impact. When they diverge, the gap turns an ambiguous topline into a sharp product decision -- build a better product, or build a better adoption funnel. 1. The Opt-In Barrier Across Demand and Supply Across the tech industry, many platform features rely on voluntary adoption. A customer chooses whether to claim a promotional discount. A traveller opts into a loyalty program. An e-commerce seller enables a smart-pricing tool. In every case, the platform cannot force adoption -- and the feature's true impact becomes hard to measure, on both demand and supply. At Booking.com this challenge spans both sides of the marketplace -- travellers choosing to log in, partners choosing to adopt new features. Unlike a search-ranking change that applies to 100% of traffic, opt-in features create a "trilemma" for Product Data Science: Voluntary Adoption (the "Opt-In" Barrier): Users must actively enable the feature. A standard A/B test cannot separate the product's effect from the motivation that drove users to adopt it. Extreme Heterogeneity: Travellers range from once-a-year holidaymakers to travel agencies booking thousands of nights; partners range from single-apartment hosts to hotel chains. This variance is noise on both sides. Finite Sample Sizes: Opt-in features target a finite sub-segment, so we cannot simply "run the test longer" to gain power. When these stack up, a flat topline can hide a strong product behind a weak adoption funnel. Genuinely strong features get killed, and resources flow into the wrong int [Next page]()