Senior Data Scientist

At SOM, we are a collective committed to shaping a better future for our clients, communities and planet. We aspire to create the most sustainable, impactful work through creative, interdisciplinary teams with all the resources and diversity of a global firm. We value those who are passionate about excellence, innovation, integrity, inclusivity, and collaboration. Together we can achieve great things. Success at SOM Means Ideas: We believe in a meritocracy of ideas. We look to everyone to actively contribute to the discourse in the firm. Knowledge: We believe that expertise is the result of lifelong learning. We are not expected to know everything, but rather to passionately pursue answers, develop new skills and deepen our knowledge. Product: We strive for excellence in the concept, quality, and delivery of our work. Individual: We value individuals who bring the highest standards of professionalism and personal integrity in the way they work. Each person can develop and contribute their wealth of attributes, skills and knowledge to support the overall health of the firm. Team: We value the power of interdisciplinary integration. A positive team culture in which everyone collaborates openly towards common goals is essential. Firm: We have a history of making transformative contributions to the profession and our communities. We are constantly innovating and attempting to bring new approaches, solutions and processes to our work. 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 busin