Vetted Predictive Modeling Professionals

“HR Business Partner with GroupM experience supporting multiple mergers, building and transitioning Analytics/Programmatic/Account Management teams through hands-on job architecture, benchmarking, and performance management. Uses HR analytics (including an Excel-based retention model with visual reporting) and cross-functional partnership with IT to reduce disruption and improve retention during restructuring and major policy changes like return-to-office.”

“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”

“Early-career engineer with Charles Schwab internship experience building and testing production-bound internal APIs, emphasizing architectural fit, stakeholder alignment, and systematic debugging. Also has academic Python/ML experience analyzing Oura Ring biometric data and exposure to multi-agent robotics through coursework and RoboSub.”

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

“AI/ML Engineer who built a production RAG-based LLM system for insurance policy documents, turning thousands of messy PDFs into a searchable index using LangChain, Azure AI Search vectors, hybrid retrieval, and FastAPI. Strong focus on evaluation (MRR/precision@k/recall@k, REGAS) and performance optimization (vLLM), with prior clinical NLP experience using BERT-based NER validated on ground-truth datasets.”

“University hackathon finalist (2nd place) who built CareerSpark, a production-style multi-agent career guidance app in 24 hours using a hierarchical debate architecture with a moderator/judge agent. Has startup internship experience at LiveSpheres AI using LangChain for multi-LLM orchestration, and demonstrates a structured approach to testing/evaluation (golden sets, integration sims, latency/accuracy KPIs) plus strong non-technical stakeholder communication.”

“Recent J&J intern who built a conversational RAG agent and led a shift from a monolithic model to a modular RAG workflow, cutting response time from several days to under a second by tackling data fragmentation, context retention, and embedding/latency optimization. Also worked on a large (7B-parameter) multimodal VQA pipeline for healthcare research and stays current via NeurIPS/ICLR and open-source contributions.”

“Data/ML practitioner with experience at Airtel and Lincoln Financial delivering measurable business outcomes: improved retention 15% via NLP sentiment analysis and cut response time ~25% using sentence-BERT + FAISS semantic linking. Strong in data quality/identity resolution (SQL + fuzzy matching) and in building production-grade Python workflows orchestrated with Airflow/AWS Glue, including validation and dashboard integration in Power BI.”

“Software engineer with experience building and iterating high-volume Spring Boot microservices on AWS (Docker/Kubernetes) and integrating with React front-ends. Also delivered an LLM-powered document summarization system using embeddings + retrieval (RAG) with grounding/guardrails and built evaluation loops that directly drove retrieval and chunking improvements. Has scaled Kafka-based pipelines processing millions of messy financial/infrastructure records with reliability and cost/latency tradeoff management.”

“Serial entrepreneur since leaving corporate in 2009, working largely for equity on multiple startups. Building (1) academically rigorous, anti-overfitting quant/backtesting tools for retail investors (with potential applicability to smaller hedge funds lacking quant staff) and (2) a partner-led “social-as-a-service” platform for verticals like real estate/PropTech (including FSBO use cases) focused on first-party data capture vs. big tech.”

“Robotics software/ML engineer who built perception and navigation-related ML systems for autonomous supermarket carts, including object detection, shelf recognition, and obstacle avoidance. Strong ROS/ROS2 practitioner who optimized real-time performance (reported 50% latency reduction) and deployed containerized ROS/ML pipelines at scale using Docker, Kubernetes, and CI/CD.”

“Built and productionized an LLM-powered assistant for company documents and support questions, focused on reducing time spent searching PDFs/policies/tickets while preventing hallucinations by grounding answers in approved sources. Demonstrates strong production engineering (Kubernetes/orchestration, caching, monitoring, fallbacks) plus security-minded permissioning and close collaboration with operations/support stakeholders.”

“Operations-focused team lead currently managing 20 coordinators, with strong workforce management experience spanning forecasting, scheduling, KPI/staffing reporting, and executive-facing data presentations. Led a cross-functional Salesforce implementation and redesigned forecasting/workflow to support a newly created internal-promotion department, improving flexibility in coverage planning.”

“Data scientist with hands-on experience building an XGBoost-based customer segmentation/churn risk scoring model used by sales and marketing teams. Emphasizes production-grade practices—efficient SQL for large-scale data pulls, rigorous data validation/testing, and scalable, modular Python code designed to support multiple customer types.”

“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”

“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”