Senior Data Scientist

 

What You’ll Do

Lead applied AI projects from concept to impact — prototype, validate, and help teams deploy practical ML and GenAI solutions. Collaborate cross-functionally: Partner with product, engineering, and research teams to scope problems, identify opportunities, and co-develop solutions. Act as an internal consultant: Advise teams on ML/AI best practices, model evaluation, and productive use of generative technologies. Design robust experiments and establish evaluation pipelines for model reliability, accuracy, and business impact. Bridge research and production: Package research insights into usable APIs, tools, or workflows for other teams. Explore new techniques (e.g., LLMs, embeddings models, retrieval-augmented generation, agentic workflows) to enhance developer and security experiences. Share knowledge and mentor peers, helping elevate the organization’s AI literacy and capabilities.

 

What We’re Looking For

6+ years of experience in applied data science, machine learning, or AI research Strong Python skills and hands-on experience with ML/AI libraries and platforms such as Databricks, OpenAI API, and Scikit-learn Comfortable working with large, messy, or unstructured datasets — you know how to turn chaos into features, insights, and beautiful visualizations Deep familiarity with LLMs and GenAI ecosystems (e.g. OpenAI, Claude, Hugging Face): skilled in prompt engineering, parameter tuning, and evaluating model behavior against ground truth Experience taking ML or GenAI systems from prototype to production, even if small-scale or incremental Strong analytical thinking, experimentation skills, and appreciation for trustworthy, data-driven evaluation Proficiency with Git and collaborative code workflows (GitHub or similar) A balanced mindset — equally comfortable exploring research ideas and implementing production-ready systems Proactive and self-directed: you don’t wait for perfect specs; you find meaningful problems and drive them to completion

 

Bonus Points

Experience with AI-assisted coding tools (Copilot, Claude Code, Codex, etc.) Familiarity with agentic workflows, Model Context Protocol (MCP), and tool-use integrations Exposure to cybersecurity, anomaly detection, or code analysis Understanding of MLOps practices (MLflow, AWS SageMaker, model serving, or monitoring)

 

Things we are proud of