Vetted Exploratory Data Analysis Professionals

“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.”

“Robotics/ML engineer with internship experience at Valeo building a deep-learning prototype to replace parts of a legacy SLAM backend for autonomous parking, focused on making models run reliably in real time on embedded hardware (quantization/distillation + TensorRT). Also brings strong MLOps/deployment experience (Docker, Kubernetes on AWS EKS, CI via GitHub Actions) and has supported patent filing by explaining the technical approach to legal stakeholders.”

“ML/NLP practitioner and technical founder who built an AUP risk-scoring model at Bill.com using TF-IDF + SVD features with XGBoost, and previously created automated data-quality guardrails for a Global Equity Risk stacked ML model at Thomson Reuters. Recently built a RAG-based chatbot for PaymentJock’s Home Affordability Probability product using embeddings and a local vector database (FAISS/Chroma), improving answer quality through chunking rather than expensive fine-tuning.”

Parth Vadera

Screened

Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps

Tracy, CA11y exp

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

Venu Dave

Screened ReferencesStrong rec.

Junior Software Development Engineer specializing in backend data platforms and LLM applications

New York, NY3y exp

“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”

“Robotics software engineer with hands-on experience building a vision-guided grasping pipeline on a 7-DOF Franka arm, implementing gradient-based IK with null-space optimization and RRT* motion planning in ROS1. Strong in sim-to-real deployment and real-world debugging—addressed frame misalignment via hand-eye calibration and centralized TF configuration, and reduced replanning/jitter by tuning a weighted pose filter using rosbag replay and variance/grasp-time metrics. Also built an ESP32-based mobile robot architecture combining embedded decision-tree control with WiFi/web high-level commands.”

“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”

Derek Tuggle

Screened

Executive Robotics & Machine Learning Engineer specializing in industrial IoT controls

San Francisco, CA6y exp

“VP of New Product Development at Axiom Cloud who built and scaled a "Virtual Battery" product that used supermarket frozen inventory as thermal energy storage—personally prototyped core control/safety logic in Python and led the engineering buildout through deployment and operations. Combines real-world industrial controls and edge deployment experience (LonWorks/Modbus, Docker/CI/CD) with an MS in CS focused on robotics, perception, and ML, including ROS 2 and YOLO-based perception.”

“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”