Staff Data Scientist

Responsibilities

• Lead end-to-end data science projects, from problem framing and data exploration through model development, deployment, and monitoring
• Design and build supervised, unsupervised, and deep learning models to solve high-impact business problems
• Develop AI agentic applications and LLM-powered solutions to automate workflows and unlock new capabilities
• Design and analyze experiments (A/B testing, causal inference) to measure model and product impact
• Perform feature engineering, data validation, and quality assurance across large, complex datasets
• Partner with data engineering and ML platform teams to productionize models and ensure scalability, reliability, and monitoring
• Translate ambiguous business questions into well-scoped data science problems and communicate findings to technical and non-technical stakeholders
• Mentor junior data scientists and analysts, and contribute to best practices across the data science team

Requirements

• 5–7 years of experience building and deploying production machine learning models, including supervised, unsupervised, and deep learning approaches
• Advanced proficiency in Python and SQL, with deep experience in ML libraries such as scikit-learn, PyTorch or TensorFlow, pandas, and NumPy
• Hands-on experience building AI agentic applications using LangChain, LangGraph, or similar frameworks, including integration with LLMs and external tools/APIs
• Hands-on experience with the full ML lifecycle: feature engineering, model training, hyperparameter tuning, evaluation, deployment, and monitoring
• Strong foundation in statistics, experimental design (A/B testing), and core ML algorithms (gradient boosting, neural networks, clustering, dimensionality reduction)
• Experience working with large-scale datasets using distributed computing frameworks (Spark, Dask) and cloud platforms (AWS, Azure, or GCP)
• Proven ability to translate ambiguous business problems into technical solutions and communicate results to both technical and non-technical stakeholders
• Experience mentoring junior data scientists in an Agile environment