Senior Data Scientist

Position Responsibilities

• Advanced Analytics & Machine Learning: Develop and implement statistical, predictive, optimization, machine learning, and AI solutions that address meaningful business and design challenges. Select methods based on the problem and available data rather than applying technology for its own sake.
• End-to-End Project Ownership: Independently drive data science projects from problem discovery and data exploration through modeling, validation, deployment, and adoption. Work effectively through ambiguity, identify missing information, and determine practical next steps with limited day-to-day direction.
• Project Management & Problem Solving: Define scope, identify risks and dependencies, break ambiguous problems into concrete deliverables, and communicate progress and blockers clearly. Use documentation, experimentation, available tools, and targeted collaboration to independently investigate unfamiliar problems.
• Predictive Modeling & Experimentation: Build, validate, and evaluate predictive and statistical models using rigorous analytical methods. Establish appropriate baselines, design experiments, select meaningful evaluation metrics, and communicate assumptions, limitations, and uncertainty.
• Production Machine Learning: Develop analytical and machine learning solutions with production use in mind, including reproducibility, testing, versioning, deployment, monitoring, and maintainability. Work within shared engineering standards and contribute to improving those standards over time.
• Data Analysis & Quality: Mine, interrogate, and combine data from multiple systems. Perform exploratory analysis, feature engineering, QA/QC, and validation to determine whether available data is reliable and appropriate for analytical use.
• Data Products & Visualization: Translate analytical work into usable applications, APIs, datasets, reports, dashboards, or other decision-support tools. Communicate findings clearly to both technical and non-technical audiences.
• Cross-Functional Collaboration: Work closely with Data Engineering, DevOps, technology teams, and business stakeholders to support secure, sustainable deployment and integration of data science solutions.
• Business & Industry Understanding: Develop expertise in SOM’s business, architecture and design practices, project delivery, and operational processes in order to identify increasingly valuable applications of data science.
• Data Governance & Documentation: Follow established security, governance, privacy, and documentation standards. Maintain clear documentation for models, analytical workflows, assumptions, dependencies, and production processes.
• Ownership: Take responsibility for outcomes, not just assigned tasks. Proactively identify problems, recommend solutions, and follow work through implementation and adoption.
• Technical Judgment: Develop and communicate informed technical opinions while remaining open to feedback, alternative approaches, and new evidence.
• Mentorship & Knowledge Sharing: Share analytical techniques and best practices with colleagues and contribute to increasing data and AI literacy across the firm.
• Continuous Improvement: Stay current with developments in data science, machine learning, AI, and analytical engineering, and help improve SOM’s tools, practices, and development standards over time.

Leadership

• Inspires and leads others by example, participates in staff mentoring and training, clearly defines team member expectations and responsibilities, empowers others, and delegates where appropriate based on team members’ recognized abilities and potential.
• Develops and/or manages, in collaboration with team members, a clear and consistent work plan to achieve the project budget, deliverables, and schedule.
• Actively engages in internal and external professional development opportunities.
• Contributes to the implementation of sustainable strategies in all project assignments and the associated development of staff knowledge.
• Contributes to the development of standards, policies, and procedures.

Minimum Qualifications

• Experience: 4+ years of professional experience in Data Science, Machine Learning, Applied Statistics, Advanced Analytics, or a closely related field. Demonstrated ownership and complexity of work are more important than years of experience alone.
• Applied Modeling: Experience developing and evaluating statistical or machine learning models that solve real-world problems and support measurable business or operational outcomes.
• Production Experience: Experience moving analytical or machine learning work beyond experimentation into production or regular business use. Familiarity with testing, reproducibility, deployment, monitoring, or related ML engineering practices.
• Programming: Strong proficiency in Python and SQL, with experience working with common data science and machine learning libraries and large datasets.
• Data Platforms: Experience working with modern cloud data platforms or analytical environments such as Databricks, Snowflake, AWS, Azure, or comparable technologies.
• Statistics & Machine Learning: Strong grounding in statistical reasoning, model evaluation, feature engineering, experimentation, predictive modeling, and appropriate machine learning techniques.
• Initiative & Resourcefulness: Demonstrated ability to work independently, investigate unfamiliar problems, learn new tools and techniques, and make progress without requiring detailed step-by-step direction.
• Judgment: Ability to evaluate multiple technical approaches and recognize when a simpler analytical or rules-based solution is more appropriate than machine learning.
• Communication: Clear and direct communicator who can collaborate effectively across technical teams and non-technical leadership.
• Collaboration & Growth: Comfortable working in a collaborative environment, receiving feedback, working within shared engineering standards, and progressively taking greater ownership as the team’s data science capability matures.
• Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related quantitative field, or equivalent professional experience.

Other Preferred Qualifications

• Experience with Databricks, Spark/PySpark, MLflow, or similar machine learning and distributed data platforms.
• Familiarity with modern analytics engineering and development practices, including dbt, Git, automated testing, and CI/CD.
• Experience building analytical applications or internal data products using tools such as Streamlit or similar frameworks.
• Experience with optimization, forecasting, experimentation, causal inference, NLP, or generative AI.
• Experience working closely with Data Engineering or ML Engineering teams to productionize models.
• Experience with cloud platforms such as AWS or Azure.
• Interest in architecture, engineering, design, sustainability, or the built environment.

Compensation for this role is based upon experience, qualifications, location, and education and typically ranges from $140,000 to $160,000.

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