Staff Data Scientist

• Design, build, and evaluate predictive and prescriptive models across customer behavior, operational efficiency, and personalization
• Apply machine learning, deep learning, and statistical techniques to real-world business challenges
• Drive end-to-end ML and GenAI projects from proof of concept through production
• Build, optimize, and deploy models into production with scientists and software engineers
• Establish scalable, automated processes for large-scale data analysis, model development, validation, and serving
• Run A/B and multivariate experiments and apply causal inference to validate hypotheses and measure impact
• Analyze complex datasets and translate findings into strategic recommendations
• Explain scientific methodology and results to technical and non-technical audiences using data visualization
• Partner with product managers and business stakeholders to identify opportunities and frame data science solutions
• Collaborate with data engineers, analysts, and software developers on scalable data-powered applications
• Serve as technical practitioner in AI/ML and lead efforts across teams
• Research innovative scientific approaches and bring them into client work
• Mentor junior data scientists
• Build reusable assets, tools, and processes to increase team velocity and impact

• 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 algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, or high-performance computing
• Experience designing and analyzing controlled experiments, such as A/B testing and 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
• AWS cloud certifications or ML specialty credentials are helpful extras
• Palantir experience is a helpful extra