Senior Data Scientist

Skidmore Owings & Merrill•Chicago, IL

About The Position

SOM is seeking a Senior Data Scientist to develop and implement data science, machine learning, statistical, optimization, and AI solutions addressing business and design challenges. The role owns projects end to end, from problem discovery and data exploration through modeling, validation, deployment, and adoption, and requires sound judgment in choosing practical methods suited to the problem and available data. The Senior Data Scientist will build predictive models and production-ready analytical solutions, assess data quality, and translate findings into useful data products such as applications, APIs, datasets, reports, and dashboards. The position collaborates with technical teams and business stakeholders, follows security, privacy, governance, and documentation standards, and helps improve SOM’s analytical practices and data and AI literacy. The role also includes leadership through mentoring, training, work planning, professional development, and contributions to sustainable strategies, standards, policies, and procedures.

Requirements

At least 4 years of professional experience in Data Science, Machine Learning, Applied Statistics, Advanced Analytics, or a closely related field.
Experience developing and evaluating statistical or machine learning models that solve real-world problems and support measurable business or operational outcomes.
Experience moving analytical or machine learning work into production or regular business use, with familiarity in testing, reproducibility, deployment, monitoring, or related ML engineering practices.
Strong proficiency in Python and SQL and experience with common data science and machine learning libraries and large datasets.
Experience with modern cloud data platforms or analytical environments such as Databricks, Snowflake, AWS, Azure, or comparable technologies.
Strong grounding in statistical reasoning, model evaluation, feature engineering, experimentation, predictive modeling, and appropriate machine learning techniques.
Ability to work independently, investigate unfamiliar problems, learn new tools and techniques, and make progress without detailed step-by-step direction.
Ability to assess technical approaches and recognize when a simpler analytical or rules-based solution is more appropriate than machine learning.
Clear, direct communication and ability to collaborate across technical teams and with non-technical leadership.
Comfort working collaboratively, receiving feedback, following shared engineering standards, and progressively taking greater ownership.
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent professional experience.

Nice To Haves

Experience with Databricks, Spark/PySpark, MLflow, or similar machine learning and distributed data platforms.
Familiarity with analytics engineering and development practices such as dbt, Git, automated testing, and CI/CD.
Experience building analytical applications or internal data products with 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.

Responsibilities

Develop and implement statistical, predictive, optimization, machine learning, and AI solutions for business and design challenges.
Own data science projects from problem discovery and data exploration through modeling, validation, deployment, and adoption.
Define project scope, identify risks and dependencies, break ambiguous problems into deliverables, and communicate progress and blockers.
Build, validate, and evaluate predictive and statistical models using rigorous methods, appropriate baselines, experiments, and evaluation metrics.
Develop production-ready analytical and machine learning solutions, including reproducibility, testing, versioning, deployment, monitoring, and maintainability.
Mine and combine data from multiple systems; perform exploratory analysis, feature engineering, data quality checks, and validation.
Translate analytical work into applications, APIs, datasets, reports, dashboards, and other decision-support tools.
Communicate findings, assumptions, limitations, and uncertainty to technical and non-technical audiences.
Collaborate with Data Engineering, DevOps, technology teams, and business stakeholders on secure and sustainable deployment and integration.
Develop understanding of SOM’s business, architecture and design practices, project delivery, and operational processes to identify valuable data science applications.
Follow security, governance, privacy, and documentation standards; document models, workflows, assumptions, dependencies, and production processes.
Take responsibility for outcomes, proactively identify problems, recommend solutions, and follow work through implementation and adoption.
Share analytical techniques and best practices, mentor and train staff, and contribute to data and AI literacy across the firm.
Stay current with data science, machine learning, AI, and analytical engineering developments and help improve tools, practices, and standards.
Lead by example, clarify team member expectations and responsibilities, empower others, and delegate where appropriate.
Develop or manage work plans with team members to achieve project budgets, deliverables, and schedules.
Participate in internal and external professional development and contribute sustainable strategies, staff knowledge, standards, policies, and procedures.