Top Data Science Jobs in Minneapolis, MN

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.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead design, development, and deployment of AI/ML models (forecasting, optimization, RL, generative AI) for pricing and promotion. Extract and integrate complex data, monitor model performance, establish AI engineering patterns, document code, and communicate results to technical and non-technical stakeholders to operationalize models across the supply chain.

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

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.

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.

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.

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.

Analyze billing patterns, healthcare claims, and complex datasets to identify anomalies, audit data integrity, and support Trust and Safety initiatives. Build risk-monitoring pipelines and compliance dashboards using SQL, Python/R, and visualization tools. Translate technical findings into risk assessments for legal, compliance, and executive partners while developing safeguards to protect patients, regulatory standing, and platform operations.

Lead design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Develop and deploy AI and machine learning solutions, analyze complex enterprise datasets, build predictive and optimization models, conduct statistical experiments, and translate findings into business recommendations. The role includes applying deep learning, NLP, generative AI, and ethical AI practices; creating scalable decision-support frameworks; evaluating emerging technologies; collaborating across teams; and mentoring junior data scientists and analysts.

Lead end-to-end generative AI and machine learning initiatives, designing secure and scalable architectures, developing and deploying advanced models, analyzing healthcare data, conducting experiments, and establishing MLOps processes. Research emerging AI methods, promote responsible AI practices, and mentor or recruit AI and machine learning talent. Collaborate with software engineers to productionize models and deliver reusable capabilities for healthcare solutions.

Provides technical and analytical leadership for enterprise data science and AI initiatives. Designs and deploys AI/ML solutions, develops predictive and causal models, leads experimentation and validation, analyzes large-scale datasets, and translates findings into executive recommendations. Establishes responsible AI, governance, explainability, monitoring, and reproducibility standards while mentoring data scientists. Applies Generative AI, LLMs, agentic AI, and advanced statistical methods to create scalable decision-intelligence capabilities and measurable business value.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Leads the architecture, development, deployment, and governance of production-grade machine learning and generative AI solutions. Responsibilities include hands-on work with traditional ML, deep learning, LLMs, NLP or computer vision, MLOps, cloud infrastructure, model security, and responsible AI. The role leads proof-of-concept initiatives, transitions models into scalable production systems, mentors technical teams, documents architecture, and advises cross-functional stakeholders and leadership.

Leads client analytics strategy and a team of data scientists, translating predictive modeling, machine learning, optimization, experimentation, and personalization into business recommendations. Sets technical direction, oversees data preparation and model delivery, and ensures data, model, and AI governance. Partners with senior leaders to drive measurable business outcomes while coaching staff, managing projects, and advancing scalable MLOps and analytics capabilities.

Leads a data science and analytics team, partners with senior business leaders, and develops scalable predictive modeling, machine learning, optimization, experimentation, and personalization solutions. Translates complex analytics into business recommendations, oversees data preparation and model performance, establishes technical direction, manages governance and compliance, and drives team hiring, coaching, and development.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Develop predictive analytics and machine learning solutions for Polaris’s Supply Chain organization, focusing on supplier risk mitigation. Responsibilities include preparing large datasets, applying statistical and machine learning methods, building and validating models, deploying solutions to cloud and edge platforms, monitoring data and model quality, and communicating actionable insights to technical and business stakeholders.

Develop and deploy reduced-order models and hybrid digital twins combining physics-based simulations, machine learning, and real-time IoT data. Responsibilities include building surrogate models, integrating telemetry pipelines, validating models against physical test data, deploying solutions to cloud and edge environments, and collaborating with engineering and software teams. The role also leads technical initiatives, mentors engineers, and advances simulation governance and modeling strategy.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.