Staff Data Scientist

As a Staff Data Scientist, you own the modeling work behind our hardest AI and machine learning problems: framing ambiguous business questions, building and validating models, and getting them into production. You bring deep applied ML and statistical rigor, the engineering judgment to ship at scale, and the credibility to explain it all to a room of executives.

You’ll work alongside scientists, data engineers, developers, and product partners across client engagements, with real autonomy over technical direction. You set the standard for scientific rigor, mentor junior data scientists, and thrive where the problem is ambiguous, the data is messy, and the answer has to work in production.

What You’ll Bring

• 5+ years of professional experience in data science, statistics, and applied machine learning, including 3+ years building models for business applications
• PhD, or a Master’s degree with 4+ years of experience, in Computer Science, Computer Engineering, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field
• Deep proficiency in Python or R, with strong skills in libraries like scikit-learn and TensorFlow/PyTorch, and fluency in models, algorithms, and ontologies
• Experience programming in Java, C++, or a related language
• Proven track record in statistical modeling, time series forecasting, NLP, or optimization
• Experience in one or more of: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, or high-performance computing
• Experience designing and analyzing controlled experiments (e.g., A/B testing, uplift modeling)
• Demonstrated success deploying models into production using APIs, pipelines, or ML frameworks
• Hands-on experience with Databricks, AWS (SageMaker), and Snowflake
• Experience building and deploying Data Mesh architectures

Helpful Extras and Unique Skills

• Palantir experience
• AWS cloud certifications or ML specialty credentials

Staff Data Scientists are where Robots and Pencils’ promise of “production-ready AI in 30 to 45 days” is backed by evidence. On the engagements you lead, you decide which approach is worth building, how success gets measured, and whether a model is ready for real users. The work moves only as fast as someone is willing to own the science, test the hypothesis, and ship the result.