Top Data Science Jobs in Minneapolis, MN

Leads the design, development, deployment, and monitoring of AI and machine learning models for supply chain decision-making. Extracts and integrates complex data, performs analysis, develops forecasting and optimization solutions, and embeds models into business workflows. Communicates findings to technical and non-technical stakeholders while partnering with business and technology teams to advance AI adoption and deliver measurable impact.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Develop machine learning, predictive, graph, geospatial, and fraud, waste, and abuse detection models for federal healthcare customers. Responsibilities include data preparation, model training, data mining, visualization, technology evaluation, and customer consultation. The role requires Python, SQL, R, cloud data management, big data, NLP, and advanced analytics expertise, with preferred experience in Databricks, AWS SageMaker, Tableau, and healthcare claims data.

The Lead AI/ML Engineer will design and implement ML models, integrate AI solutions, and collaborate with technical teams to enhance AI capabilities, focusing on healthcare applications.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.

Develop predictive, prescriptive, forecasting, valuation, and portfolio-performance models supporting collections, acquisitions, and business strategy. Build production data pipelines, transformations, analytical workflows, data models, quality checks, and monitoring frameworks using SQL, Python, and/or R. Perform exploratory analysis, create dashboards and reports, translate business needs into technical solutions, and collaborate with analytics, engineering, and business stakeholders.

Develop and refine credit risk strategies, rules, monitoring tools, and performance dashboards. Use SQL, Python, statistics, and data science to analyze complex datasets, identify risk trends, optimize processes, and influence business decisions. Collaborate with product, engineering, and operations teams to implement strategies across the customer lifecycle, while mentoring junior team members.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative reasoning problems. Create expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling expertise.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Create reproducible data science and machine learning tasks with verified reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify implementation errors and data leakage, refine task designs, and document requirements and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical expertise in implementing and validating solutions.

Build and validate forecasting, optimization, anomaly detection, and measurement models using complex customer data. Perform exploratory analysis, experiments, backtesting, and error analysis; partner with ML and data engineers to productionize models, features, evaluation pipelines, and monitoring. Translate business questions into analytical recommendations and communicate findings to technical and non-technical stakeholders. The role is hybrid in New York City or remote in the U.S. with monthly travel to New York.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Leads the architecture, development, deployment, and governance of production machine learning and generative AI solutions. The role drives GenAI proof-of-concepts, establishes MLOps and responsible AI practices, mentors engineers, conducts technical reviews, and collaborates with cross-functional teams to deliver scalable AI capabilities. Expertise is required in statistical methods, deep learning, NLP or computer vision, cloud-native infrastructure, and production ML systems.

Conducts multi-omics analyses of organoid and normal tissue data, including single-cell and bulk RNA sequencing, proteomics, and metabolomics. Develops computational pipelines for processing, quality control, statistical analysis, and organoid benchmarking. Collaborates with wet-lab teams on experimental design and protocol optimization, interprets findings, contributes to manuscripts, and presents research at scientific conferences.

Develop data pipelines, organize and analyze structured and unstructured government data, create visualizations, build AI agents and lightweight automations, and occasionally develop predictive models. The role involves consulting with senior stakeholders, communicating actionable insights, supporting data capacity building, and collaborating with product, design, engineering, and research teams in a remote government-services environment.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Analyze recruiting and interview data to improve AI-powered candidate evaluation systems. Build metrics, statistical and machine learning models, LLM evaluation datasets, experiments, dashboards, and reproducible data workflows. Collaborate with product and engineering teams on candidate matching, interview quality, recruiting analytics, bias detection, and AI system improvements.