Staff Data Scientist

Role overview Lead applied data science work on complex machine learning and AI problems, from framing an ambiguous business question to validating and deploying a solution. You will set scientific direction on client engagements, collaborate across product and engineering teams, and explain results clearly to technical and business audiences.

Responsibilities - Design and evaluate predictive or prescriptive models for challenges such as customer behavior, operational performance, and personalization. - Lead machine learning and generative AI projects from proof of concept through production, including model optimization and deployment. - Establish scalable processes for data analysis, model development, validation, and serving. - Use controlled experiments, multivariate testing, and causal inference to test hypotheses and measure outcomes. - Analyze complex datasets and turn findings into recommendations, communicating methods and results with clear visualizations. - Guide technical work across teams, mentor data scientists, research useful approaches, and create reusable tools and processes.

Requirements - At least five years of professional data science, statistics, or applied machine learning experience, including three or more years building business models. - A PhD, or a master’s degree with at least four years of experience, in a quantitative field such as computer science, statistics, mathematics, engineering, or machine learning. - Strong Python or R skills and experience with libraries such as scikit-learn and TensorFlow or PyTorch; familiarity with algorithms and ontologies. - Programming experience in Java, C++, or a related language. - Applied modeling experience in areas such as statistical methods, forecasting, NLP, or optimization. - Experience designing experiments, deploying models through APIs, pipelines, or ML frameworks, and working with Databricks, AWS SageMaker, and Snowflake. - Experience building and deploying Data Mesh architectures.

Nice to have - Palantir experience or AWS cloud and machine learning certifications.

Benefits and work setup - Work across client engagements with scientists, data engineers, developers, and product partners. - The role offers autonomy over technical direction and includes mentoring and cross-team leadership.