Vetted Predictive Modeling Professionals

Poorna Pedapudi

Screened

Mid-Level Software Engineer specializing in distributed backend systems and cloud-native microservices

Seattle, WA5y exp

“Software engineer focused on data platforms and applied LLM systems: built an internal data quality monitoring layer to catch silent data drift and iterated post-launch after finding ~30% false-positive alerts, reducing noise via dynamic baselines and improved structured logging. Also shipped a production RAG-based internal knowledge assistant over Jira/Confluence with citations, confidence-based fallbacks, and nightly automated evals to prevent regressions.”

“Backend/AI systems engineer who has shipped production LLM agents focused on prompt engineering, code generation, and incident-response automation. Stands out for combining strong agent orchestration and reliability engineering with measurable business impact, including 60-70% cost reductions, 45% lower monthly LLM spend, and a 5x increase in developer iteration speed.”

“Building a confidential startup in the software security space (no capital raised yet). Has been learning the VC/accelerator landscape through a network of founders and independent research, with a focus on investor readiness and relationship-driven access to VCs. Motivated to found due to repeated layoffs and late-stage hiring pauses, seeking more control while prioritizing family financial stability.”

“Analytics professional with experience at McKinsey & Company and Dell Technologies, focused on turning messy operational and business data into trusted dashboards and decision tools. They combine SQL, Power BI, and Python to solve data quality issues, define metrics like retention, and deliver measurable impact such as a roughly 30% reduction in manual reporting time.”

“Growth creative lead at Amazon who ran structured, incrementality-informed A/B tests for Prime acquisition on Amazon DSP. Produced a winning benefit-led video + social-proof framework that improved CTR/CVR while cutting CPA (-24%) and increasing ROAS (+32%), then scaled the approach across email and onsite placements for an additional 10–12% lift in Prime sign-ups.”

“Active in the venture ecosystem as a Rogue Women's Fund fellow and angel investor, with memberships in Gaingels and Angel Squad (HustleFund). Interested in founding a company to leverage extensive experience, and evaluates ideas through market need and economic viability of the target population.”

Mayuna Gupta

Screened

Senior Data Scientist specializing in computer vision and medical imaging

San Diego, CA4y exp

“Built and deployed an LLM-powered RAG system (PubChemRAG) for a 4D Mitospace project to compare mechanisms across a ~100-drug glossary and surface expected pathway/phenotypic differences in mitochondrial imaging. Worked closely with biochemistry and microscopy experts to design tiered evaluation benchmarks, iterating on prompts, retrieval quality (corpus hygiene, chunking strategy), and model outputs under GPU constraints using LangChain.”

“Solo technical founder building LabInsights, a healthcare middleware platform aimed at reducing missed follow-ups for abnormal lab results by layering AI-driven flagging, patient-friendly education, and prioritized care-team workflows on top of existing hospital systems. Currently validating in Bangalore with a former hospital CEO advisor, focusing on unit economics and securing LOIs/paid pilots; attracted to a venture studio to fill GTM, fundraising, and ops gaps.”

“Robotics/computer-vision engineer with industrial safety monitoring experience, building real-time pose estimation (TRTPose) and 2D-to-3D localization and optimizing pipelines to sustain 30+ FPS under heavy multi-entity load. Also brings edge-to-cloud distributed systems work (HoloLens + Google Vision/Translation) and production ML deployment experience using Docker/CI/CD across finance and edge camera environments.”

“Primary engineer at Customer Insights AI who built an end-to-end Python pipeline for 340B drug pricing compliance, using ML to detect suspicious pharmaceutical claims and benefit diversion. Stands out for combining healthcare compliance domain knowledge with production reliability practices, and for turning ambiguous analyst-driven review processes into automated workflows that cut manual review by 70%.”