Vetted Predictive Modeling Professionals

“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”

“Financial engineering analyst at BlackRock who has built internal data products for thousands of sales users, including an end-to-end fund comparison system that combines internal and third-party data into compliance-approved PDFs. Brings a mix of data engineering, stakeholder-heavy delivery, and emerging AI experimentation, with current work focused on regression testing and Snowflake-based portfolio data automation.”

“Senior product leader with experience at Constant Contact, Retention Science, eBay, and Ticketmaster, spanning PLG transformation, AI-driven marketing automation, UX turnarounds, and complex platform migrations. Particularly compelling for roles needing a product executive who can tie user insight, experimentation, and cross-functional execution directly to conversion, revenue, and team growth.”

“Senior product leader from PlayStation and Epic Games with rare experience scaling consumer gaming subscription and personalization platforms at massive scale. Led the PlayStation Plus evolution into a tiered live-service platform, driving growth from 30M to 40M subscribers and a 15% ARPU lift, while also bringing AI/ML personalization expertise from the Epic mobile store and Fortnite ecosystem.”

“Intel veteran who applied multiple linear regression and time-series drift analysis to semiconductor lithography overlay/metrology data, feeding model outputs into automated process control. Comfortable working across Python, VBA, and JMP/JSL, with a pragmatic approach to validation (RMSE + trend visualization) and data quality via close coordination with measurement/metrology teams.”

Anand Vallamsetla

Screened

Executive Engineering Leader & Systems Architect specializing in AI, cloud platforms, and FinTech

Los Altos, CA26y exp

“Operator with 20+ years experience and a business degree who led the build and deployment of a Medicaid fraud investigation product for Texas HHSC OIG, cutting investigation time from ~2 years to 90 days. Has government go-to-market experience and has raised a friends-and-family round for an earlier startup concept; now pivoting toward a healthcare-focused venture after a cofounder with core tech could not continue.”

Michael Smith

Screened

Principal-level finance leader specializing in venture-backed startups and strategic finance

New York, NY16y exp

“Repeat founder and former venture investor with 7 years of investing experience, now building a startup focused on fixing CPG trade promotion inefficiencies. The candidate articulates a differentiated product vision using predictive modeling and portfolio-wide cross-elasticity analysis rather than event-by-event TPM measurement, and is actively mapping the NY venture studio ecosystem to support 0-to-1 company building.”

Ahmed Sadaqat

Screened

Senior Machine Learning Engineer specializing in production ML and predictive analytics

Los Angeles, CA7y exp

“ML/AI engineering leader who has owned end-to-end production systems from experimentation through deployment, monitoring, and iteration at meaningful scale. They describe running a 1M+ records/day prediction platform with 99.9% availability, shipping a RAG-based conversational AI feature for 50,000 active users, and consistently improving precision, latency, reliability, and cost with measurable business impact.”

“Senior product leader with experience at Amazon, FINRA, and Goldman Sachs, spanning large-scale data platforms, AI/ML, consumer UX, and regulated enterprise systems. Particularly compelling for roles requiring both technical depth and organizational influence: they’ve shipped products at massive scale, improved fraud models to 95%+ accuracy, and delivered measurable business outcomes while keeping human experience central to AI-driven decisions.”