{"id":3785653,"url":"https://alion.io/job/som-senior-data-scientist","title":"Senior Data Scientist","company":{"id":42416,"name":"SOM","domain":"som.com","url":"https://alion.io/company/som-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":7,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chicago, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":140000,"max":160000,"currency":"USD","period":"year","gross":null,"usd_annual":160000},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"CI/CD","optional":true},{"name":"dbt","optional":true},{"name":"Git","optional":true},{"name":"MLFlow","optional":true},{"name":"NLP","optional":true},{"name":"pySpark","optional":true},{"name":"Spark","optional":true},{"name":"Streamlit","optional":true}],"status":"live","first_seen_at":"2026-11-04T00:00:00Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-07T01:55:22Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"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.\nSuccess at SOM Means\nIdeas: We believe in a meritocracy of ideas. We look to everyone to actively contribute to the discourse in the firm.\nKnowledge: 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.\nProduct: We strive for excellence in the concept, quality, and delivery of our work.\nIndividual: 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.\nTeam: We value the power of interdisciplinary integration. A positive team culture in which everyone collaborates openly towards common goals is essential.\nFirm: 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.\nPosition Responsibilities\nAdvanced 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.\nEnd-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.\nProject 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.\nPredictive 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.\nProduction 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.\nData 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.\nData 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.\nCross-Functional Collaboration: Work closely with Data Engineering, DevOps, technology teams, and business stakeholders to support secure, sustainable deployment and integration of data science solutions.\nBusiness & 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.\nData Governance & Documentation: Follow established security, governance, privacy, and documentation standards. Maintain clear documentation for models, analytical workflows, assumptions, dependencies, and production processes.\nOwnership: Take responsibility for outcomes, not just assigned tasks. Proactively identify problems, recommend solutions, and follow work through implementation and adoption.\nTechnical Judgment: Develop and communicate informed technical opinions while remaining open to feedback, alternative approaches, and new evidence.\nMentorship & Knowledge Sharing: Share analytical techniques and best practices with colleagues and contribute to increasing data and AI literacy across the firm.\nContinuous 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.\nLeadership\nInspires 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.\nDevelops and/or manages, in collaboration with team members, a clear and consistent work plan to achieve the project budget, deliverables, and schedule.\nActively engages in internal and external professional development opportunities.\nContributes to the implementation of sustainable strategies in all project assignments and the associated development of staff knowledge.\nContributes to the development of standards, policies, and procedures.\nMinimum Qualifications\nExperience: 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.\nApplied Modeling: Experience developing and evaluating statistical or machine learning models that solve real-world problems and support measurable business or operational outcomes.\nProduction 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.\nProgramming: Strong proficiency in Python and SQL, with experience working with common data science and machine learning libraries and large datasets.\nData Platforms: Experience working with modern cloud data platforms or analytical environments such as Databricks, Snowflake, AWS, Azure, or comparable technologies.\nStatistics & Machine Learning: Strong grounding in statistical reasoning, model evaluation, feature engineering, experimentation, predictive modeling, and appropriate machine learning techniques.\nInitiative & Resourcefulness: Demonstrated ability to work independently, investigate unfamiliar problems, learn new tools and techniques, and make progress without requiring detailed step-by-step direction.\nJudgment: Ability to evaluate multiple technical approaches and recognize when a simpler analytical or rules-based solution is more appropriate than machine learning.\nCommunication: Clear and direct communicator who can collaborate effectively across technical teams and non-technical leadership.\nCollaboration & 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.\nEducation: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related quantitative field, or equivalent professional experience.\nOther Preferred Qualifications\nExperience with Databricks, Spark/PySpark, MLflow, or similar machine learning and distributed data platforms.\nFamiliarity with modern analytics engineering and development practices, including dbt, Git, automated testing, and CI/CD.\nExperience building analytical applications or internal data products using tools such as Streamlit or similar frameworks.\nExperience with optimization, forecasting, experimentation, causal inference, NLP, or generative AI.\nExperience working closely with Data Engineering or ML Engineering teams to productionize models.\nExperience with cloud platforms such as AWS or Azure.\nInterest in architecture, engineering, design, sustainability, or the built environment.\nCompensation for this role is based upon experience, qualifications, location, and education and typically ranges from $140,000 to $160,000.\nFor more about SOM: www.som.com","description_format":"text","description_chars":8540,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Professional development"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Design & Creative","Real Estate","Science & 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