Nima Sammaknejad
Tech Lead Data Science, Machine Learning & Generative Ai @Genentech
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WORK HISTORY
Tech Lead Data Science, Machine Learning & Generative Ai @Genentech
Project: RL-Driven AI Agents and RAG Pipelines to Accelerate Data-to-Insight * Technical lead (10+ cross-functional team); Fully automated data science workflows and jointly recognized with the preceding project as the winner of Dataiku Frontrunner Awards 2025 - AI for Healthcare & Life Sciences> Sub-project 1 — RL-Driven AI Agent: Developed and deployed a RL-driven AI agent using MDPs, value iteration and LangGraph/LangSmith to optimize preprocessing and modeling workflows Fully automated model-development workflows, achieving 100% reduction in manual steps > Sub-project 2 — RAG System: Built a search engine for knowledge retrieval using chunking, embedding, storage, semantic search and LangChain to support decision making Directed LLM-as-judge evaluations, yielding higher retrieval efficiency> Impact featured in LinkedIn Articles and public report-out https://zenodo.org/records/17••••02
EDUCATION
University of Tehran
BSc, Chemical Engineering
University of Alberta
PhD, Systems and Controls Engineering - Specialization: Computer Science & Stochastic Modeling
Sharif University of Technology
MSc, Systems and Controls Engineering - Specialization: Time-Series Analysis & Model Predictive Control
SKILLS
ABOUT NIMA SAMMAKNEJAD
My job in Genentech lies at the intersection of data science, ML/AI and process systems engineering, acting as the technical lead and product owner for multiple global projects.Summary of my qualifications are as follows:> 10+ years experience delivering high-impact, real-time production-grade ML/AI, Deep Learning (DL), and data-science solutions supporting 5+ global sites / users> Proven technical leadership driving initiatives through Agile execution across global teams, delivering data products with $30M+ annual impact> Hands-on experience with LLM, conversational AI and AI agent design frameworks (LangChain, LangGraph, LangSmith), Reinforcement Learning (RL)-driven agents and RAG pipelines > Deep experience across end-to-end data science methods: sampling strategies, A/B testing, multivariate statistical modeling, root-cause analysis, time-series, and platforms (Dataiku, KNIME, SAS)> Hands-on experience building batch/real-time data pipelines using GCP/AWS cloud services (Pub/Sub, Cloud Functions, Dataflow, Bigtable, BigQuery, Vertex AI), CI/CD/MLOps and Docker/Kubernetes> Strong foundation in Data Structures & Algorithms, SQL and scripting languages Python/R/MATLAB> Ph.D. & M.Sc. in Systems & Controls Engineering; expertise in computer sience, stochastic modeling, and Model Predictive Control (MPC), 15+ publications, patent applications,~400 citations
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