Vetted Data Governance Professionals

“Analytics professional with experience at Franklin Templeton and IQVIA India, focused on turning messy cross-system data into trusted reporting and actionable business insights. Stands out for combining SQL, Python, AWS ETL, and BI dashboards to solve data quality issues, improve investor engagement analysis, and standardize commercial reporting in financial services and pharma contexts.”

SAITEJA MALLEMPUDI

Screened

Senior Data Scientist and AI/ML Engineer specializing in GenAI and cloud ML

Chicago, IL6y exp

“ML/AI engineer with hands-on experience owning systems from experimentation through deployment and monitoring, including a Bank of Montreal project that improved timely interventions by 12%. Also brings GenAI/RAG experience with evaluation and safety guardrails, plus clinical NLP pipeline work extracting medication data from notes for patient risk prediction.”

“Healthcare product leader who started as a front-end engineer/unofficial product head and helped transform a custom hospital software business into a SaaS platform that became the company's flagship, generating over 80% of revenue. Later, as VP of Product after acquisition by Press Ganey, they led multi-product consolidation across global teams and launched HIPAA-compliant genAI features that delivered measurable customer efficiency gains.”

“Healthcare product leader with substantial experience in clinical software, medical devices, analytics, and AI/ML-enabled workflows. They have led a cloud analytics platform for hospital systems, managed a machine-learning insulin dosing product in a safety-critical environment, and driven UX modernization of clinician-facing applications while coordinating deeply across engineering, QA, data science, DevOps, and customer success.”

“Database/platform engineer with hands-on ownership of large-scale GCP data systems in financial services, including customer-facing SaaS investment products with strict SLAs. Stands out for leading an on-prem-to-GCP modernization using Spanner, AlloyDB, Bigtable, and BigQuery, and for building Terraform/Python automation that cut provisioning time by ~70% while improving reliability and self-service.”

“Built and deployed production LLM-powered document summarization/classification systems using embeddings, vector databases (RAG-style retrieval), and automated evaluation (BERTScore/ROUGE), with a focus on monitoring and scalable cloud pipelines. Also partnered with a fraud analytics team to deliver a transaction anomaly detection solution, translating model outputs into Power BI dashboards and actionable KPIs while iterating on thresholds and alerts based on stakeholder feedback.”

“Senior technology leader and hands-on builder spanning enterprise architecture and product/engineering leadership across healthcare and entertainment. Has led high-impact cloud and security architecture decisions (including establishing a private cloud to address scalability/security at massive scale) and scaled orgs 300% using pod-based team structures. Currently building an AI-supported hydroponics/vertical farming IoT framework (ESP32 + Azure) and a musician collaboration platform (React + Neo4j + AWS).”

“Healthcare data/ML practitioner with experience at UnitedHealth Group building production ETL and streaming pipelines (Python, BigQuery, Kafka) that unify EHR, IoT device, and lab data for patient risk prediction. Also implemented embedding-based semantic search/linking for noisy clinical notes via domain adaptation and rigorous validation with clinical stakeholders; previously built churn prediction at DirecTV using XGBoost.”

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“Data/MLOps engineer (Cognizant background) who owned an AWS/Airflow/Snowflake healthcare transactions pipeline processing ~8–10M records/day and cut pipeline/data-quality incidents by ~33%. Also built and deployed a production FastAPI model-inference service on Kubernetes (Docker, HPA) with strong observability (Prometheus/Grafana), versioned endpoints, and resilient backfill/idempotent external data ingestion patterns.”

“Data engineer with experience across healthcare and geospatial risk systems, owning end-to-end pipelines from ingestion through serving on AWS/Azure stacks. Built HIPAA-compliant data quality gates and CDC for millions of daily claims, and also delivered a real-time wildfire risk platform with 20-minute refresh cycles and a 60% data accuracy lift. Strong in streaming (Kafka), Spark performance tuning, and production-grade orchestration/CI/CD (Airflow, Docker, Jenkins, GitHub Actions, Terraform).”

“Data engineer at CenterPoint Energy who built and operated multiple production-grade GCP data systems: a daily Snowflake→BigQuery replication framework (150+ tables) with Monte Carlo/Atlan-driven observability and schema-drift protection, plus a FastAPI metrics service for pipeline health. Demonstrated measurable impact (40% faster dashboard queries, 70% less manual refresh work, zero data loss) and strong operational rigor (scaling Cloud Run jobs, SAP SLT reconciliation, quarantine patterns, CI/CD via GitHub Actions + Terraform).”

“Data engineer with experience at Capital One and HSBC building and operating GCP-based data platforms. Led an end-to-end Oracle-to-BigQuery migration processing ~200–300GB/day using Dataflow/Beam, Airflow, Dataproc/PySpark, and Looker, achieving ~99.5% pipeline success and ~30% fewer data quality issues. Strong in production reliability, schema drift handling for external APIs, and BigQuery performance/serving patterns (materialized views, authorized views, versioned datasets).”

“Has worked across multiple startup stages from pre-funding through Series D and emphasizes rigorous idea validation through direct conversations with both end users and purchasing decision-makers. Interested in applying NLP to automate summarization/abstracting of highly technical articles, with a balanced view of entrepreneurship that prioritizes health and family.”

“Built and deployed a GenAI-powered PhD career intelligence platform at NYU that maps academic backgrounds to career paths and converts long academic CVs into job-ready resumes. Stands out for treating LLM systems as structured production pipelines—combining NLP extraction, embeddings, orchestration, and AWS deployment—to improve recommendation quality and cut resume preparation time by 70%.”

“Healthcare-focused data engineer/ML practitioner with experience at Lightbeam Health Solutions and Humana building production entity-resolution and semantic similarity pipelines across EMR, lab, and claims data. Uses NLP/ML (spaCy, scikit-learn, BioBERT/LightGBM) plus Snowflake/Airflow and vector search (Pinecone) to improve linkage accuracy (reported 90%) and semantic match quality (reported +12–15%), while reducing manual cleanup by 40%+.”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

“Engineering Manager/player-coach leading Data Infrastructure, ML/DS, and AI Engineering pods who recently shipped multiple production agentic GenAI features. Built privacy-preserving LLM workflows (PII redaction via Microsoft Presidio) and drove an AI expense-approval agent from ambiguous ask to GA, cutting approval time from ~2.5 days to <4 hours with >85% accuracy. Also owned a major LLM cost overrun incident and implemented cost observability plus circuit breakers to prevent runaway agent loops.”

Chris Colinsky

Screened

Executive Technology Leader/CTO specializing in data platforms, AI agents, and e-commerce/payments

Los Angeles, CA23y exp

“Engineering leader with hands-on coding time who has driven major commerce and data-platform transformations: defined goop’s omnichannel strategy, unified payments to Square, and rebuilt real-time NetSuite inventory flows plus forecasting tools. Currently reorganized engineering into Product/Data/Support teams to hit aggressive seasonal roadmaps, and led a data-lake/medallion ELT refactor feeding embedded analytics (Tinybird) with improved reliability and cost efficiency; also accelerates onboarding via AI coding tools in a serverless, event-driven architecture.”

Kristina Colclasure

Screened

Senior HR & Talent Acquisition Operations Leader specializing in global HRIS and analytics

Downers Grove, IL20y exp

“Talent Acquisition/Talent Operations leader who has managed multi-site teams (8–15) and rebuilt high-volume recruiting workflows end-to-end, combining process rigor (structured intake, SLAs, standardized interview kits) with deep ATS analytics. Hands-on with Lever and Workday Recruiting, and led a Workday + GoodTime implementation delivering major efficiency gains (40% scheduling improvement, 50% less manual coordination) alongside measurable hiring outcomes (33% faster time-to-fill, 42% increase in DEI hiring).”

“Data engineer focused on reliability and data quality, owning end-to-end pipelines processing ~100k–300k records/day. Implemented robust validation and monitoring that cut reporting issues by ~30%, and built stable external data collection with anti-bot measures, backfills, and schema-change detection while maintaining backward-compatible internal data services.”

“Marketing analytics candidate with experience at WPP and on a global Coca-Cola campaign, focused on turning messy multi-platform media data into trusted reporting and decision systems. They combine hands-on SQL/Python pipeline building with stakeholder KPI alignment, and cite a 22% improvement in media effectiveness plus faster budget reallocation through daily automated reporting.”

“Analytics professional with higher education experience at the University of Dayton, focused on turning inconsistent operational data into standardized metrics and recurring dashboards. They combine SQL, Python, and Power BI to automate reporting, improve data integrity, and reduce manual reporting by 30%, with outputs adopted in semester planning and cross-department performance tracking.”

“Built and deployed a compliance-sensitive LLM pipeline that extracts rebate logic from hospital–supplier medical contracts, using multi-layer redaction (regex/NER/dictionary), schema-validated structured outputs, and secure placeholder reinsertion. Hosted models on Amazon Bedrock to avoid retraining on sensitive data and improved both accuracy and cost by splitting the workflow into a lightweight section classifier plus a fine-tuned extraction model, orchestrated with LangChain and evaluated via layered, test-driven agent assessments.”