Top Senior Data Science Jobs in Minneapolis, MN

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Develop and maintain predictive models for LTV, churn, retention, segmentation, personalization, and user acquisition decisions. Design experiments and causal analyses, apply LLMs to unstructured data, build AI-powered analytics tools, and productionize models with data engineering partners. Establish modeling standards, communicate insights to stakeholders, and mentor analysts and data scientists.

Leads advanced data science and machine learning initiatives for healthcare payment integrity, including fraud, waste, and abuse detection, anomaly identification, predictive modeling, statistical analysis, dashboards, and recovery investigations. Provides technical leadership, mentors analysts, integrates production analytical solutions, collaborates with legal and business stakeholders, and presents findings to leadership. Requires healthcare expertise, advanced SQL and programming skills, and knowledge of claims, billing, reimbursement, and program integrity.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Own the scientific and product roadmap for monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead cross-functional delivery with Product, Engineering, Sales, and GTM; define measurement standards, evaluate trade-offs, support customer pilots, influence commercial decisions, and turn bespoke studies into scalable products. Remain hands-on in SQL, Python, statistical modeling, and causal inference while mentoring scientists and establishing reusable methods.

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Develop scalable operations research and data science solutions using optimization, simulation, machine learning, and information retrieval. Partner with technical and business teams to frame problems, build production-ready analytical models, evaluate performance, recommend technical roadmaps, and deliver actionable insights. Lead data science practices, communicate recommendations, improve model quality, and establish long-term processes, frameworks, tools, and standards.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Build and deploy mechanistic, hybrid, and data-driven models for pharmaceutical CMC processes, including drug substance, drug product, packaging, labeling, technology transfer, and manufacturing. Integrate process and experimental data into FAIR data products, analytics, dashboards, and AI-enabled tools. Partner with technical teams and CDMOs to support process understanding, forecasting, risk assessment, regulatory filings, and technology-transfer decisions while owning the digital roadmap and mentoring colleagues.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Lead end-to-end AI/ML initiatives that improve student retention, engagement, enrollment, and re-engagement. Build and deploy predictive models, next-best-action logic, and production MLOps workflows using Databricks, MLflow, dbt, and Dagster. Own experimentation, monitoring, performance measurement, cross-functional implementation, vendor coordination, and executive communication. Mentor teammates and drive measurable outcomes across Product, Engineering, CX, and university partners.

Leads the data science platform and machine learning strategy, building data marts, predictive models, MLOps infrastructure, and analytics capabilities. Analyzes healthcare claims, clinical, workflow, and operational data to risk-stratify patients, assess clinical program impact, support dashboards, and improve patient and provider engagement. Collaborates with clinical, operations, finance, product, executive, and technology teams while serving as a trusted analytics expert for a business workstream.

Lead product-focused data science and machine learning initiatives, including exploratory analysis, production model development, evaluation frameworks, generative AI, graph-based machine learning, recommendation systems, and personalization. Collaborate with engineering, product, and business teams to deliver high-impact solutions, influence strategy, and communicate insights to technical and non-technical stakeholders. The hybrid role requires four days per week in the Greater Boston area.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.