Staff Data Scientist

Requirements

• 8+ years of experience in Data Science, Machine Learning, or a highly quantitative field, with a track record of deploying models that drove significant revenue or cost savings
• Mastery of System Design and state-of-the-art time-series research
• Leadership without Authority: Proven ability to influence executive leadership and mentor senior technical staff. Proven ability to lead large-scale technical projects across multiple teams without being a direct people manager
• Deep Technical Stack: * Expertise in Python and SQL
• Advanced experience with AWS and Databricks
• Proficiency in distributed computing (e.g., Spark, Ray) and modern data platforms (Snowflake/Databricks)
• Modern AI Toolkit: Hands-on experience with LLM orchestration (LangChain, Haystack), vector databases, and PyTorch or TensorFlow
• System Design: Strong understanding of software engineering principles (microservices, API design) as they relate to deploying ML at scale
• Education: Master’s or PhD in Computer Science, Physics, Statistics, Mathematics, or a related quantitative field
• Communication: Ability to persuade executive leadership on technical investments and "sell" a long-term technical vision

What the job involves

• Architectural Strategy & Forecasting Leadership:
• Define the long-term forecasting roadmap and establish architectural standards for the entire company
• Lead the architectural design of complex ML systems, ensuring they are scalable, maintainable, and integrated seamlessly with the broader engineering stack
• Architect reusable, "platform-level" forecasting frameworks to be used by other Data Science teams
• High-Impact Modeling:
• Own the development of "tier-1" models—those with the highest business risk or technical complexity—using advanced statistical, deep learning, or Reinforcement Learning techniques
• Tackle high-ambiguity challenges, such as integrating causal inference or deep learning into global forecasts
• GenAI & LLM Innovation:
• Act as the subject matter expert for Generative AI; design RAG architectures, evaluate foundation models, and establish fine-tuning protocols for proprietary data
• MLOps & Quality Assurance:
• Define the team’s technical standards for CI/CD, model versioning, and automated testing
• Audit codebases to ensure high-performance and "production-ready" quality
• Strategic Experimentation:
• Own the end-to-end design and evaluation of high-impact product and business experiments, establishing rigorous statistical standards and frameworks to ensure trustworthy, data-driven decisions
• Technical Mentorship & Influence:
• Provide deep technical guidance to more junior Data Scientists
• Influence the roadmap by identifying "blind spots" in current data strategy and proposing novel solutions
• Cross-Functional Translation & Ethics:
• Partner with Data Engineering to optimize data pipelines for ML and with Product to ensure technical feasibility of the long-term vision
• Lead the implementation of Model Interpretability (XAI) frameworks to ensure all automated decisions are transparent and unbiased