Overview
As Lead Applied Scientist, you will steer the Data Science and AI Delivery team in solving business problems with advanced analytics and AI. You will set scientific standards, guide design and build of AI/ML solutions, and ensure rigorous evaluation, validation, and production monitoring. You’ll work closely with engineering to align architecture and deployment while maintaining high standards for model risk and responsible AI. This role offers the chance to shape cutting-edge, tool-using AI systems that drive informed decisions and operational efficiency. You will lead through influence, mentor teammates, and communicate complex results to senior stakeholders, making a measurable impact on,
Pay / Benefits
- competitive base salary
- comprehensive benefits package
- retirement plans
- tuition reimbursement
- paid vacation
- incentive compensation
Responsibilities
- Lead design and development of AI/ML solutions and frame business problems with clear success criteria
- Design generative and agentic AI solutions (prompting, retrieval augmented generation, tool use, multi-step workflows)
- Select appropriate approaches (LLMs, deep learning, statistical methods) for each problem
- Own evaluation/accuracy methodology, metrics, test sets, thresholds, and production drift monitoring
- Lead responsible AI work (bias testing, explainability, regulatory validation)
- Solve challenging problems (information extraction from unstructured docs, automated expert workflows, optimization, forecasting, portfolio/claims analytics)
- Oversee scientific design decisions and collaborate with engineering on architecture/deployment
- Produce production-quality code and contribute to novel/high-risk work
- Set team standards for experimentation, evaluation, coding, and documentation
- Explain methods and trade-offs to business stakeholders, risk, and governance forums
Key requirements
- Strong hands-on experience with large language models and generative AI (prompt engineering, RAG, fine-tuning, agentic systems)
- Proficiency in Python and AI/data science tools (pandas, NumPy, scikit-learn)
- Solid mathematical/statistical foundations (probability, statistics, linear algebra, optimization)
- Experience with model validation and experimental design in AI systems
- Track record of delivering real-world AI solutions from prototype to production
- Practical experience with Databricks, MLflow, Spark
- Knowledge of responsible AI methods (bias/fairness, explainability, model risk)
- Ability to explain complex concepts to non-technical audiences
- Experience setting technical standards and mentoring teammates
- Effective communication with senior stakeholders
- Strong mentoring and leadership
- Strategic problem solving and decision making
- Large language models
- Generative AI
- Prompt engineering
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