Top Data Science Jobs in Minneapolis, MN

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.

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.

The Senior AI & Data Scientist leads AI model development, data preparation, analysis, and deployment to enhance decision-making in the supply chain.

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 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.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

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 for Medicaid quality programs: build analytical frameworks, perform data analyses and causal inference to evaluate program impact, translate requirements into scalable data models, collaborate with cross-functional teams, and communicate findings and risks to stakeholders to drive quality improvements.

Develop and execute analyses, predictive models, and reporting to improve Medicare Star Ratings. Analyze large healthcare datasets, extract actionable insights, partner with clinical/operations/product/IT teams, and present recommendations to stakeholders.

Lead development of advanced AI/ML and Generative AI solutions (including LLMs and RAG) to solve healthcare and growth business problems. Translate business needs into data models, combine diverse datasets, build production ML systems, mentor data scientists, collaborate with stakeholders and engineering to operationalize scalable AI/ML, and deliver executive-ready insights and recommendations.

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.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

Build and validate predictive and causal inference models from messy healthcare commercial data; translate models into dashboards/scoring systems; partner with marketing, market access, and commercial teams to inform strategy and deploy model-driven decisions.

Lead AML model development, optimization, validation, and governance for transaction and customer screening across banking, brokerage, and lending. Build ML/statistical models, maintain vendor solutions, ensure regulatory compliance, document methodologies, produce AML dashboards, and support audits, risk assessments, and stakeholder communications.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Design, develop, and deploy predictive and machine learning models for healthcare products. Extract, clean, and transform data using Python and SQL; perform EDA; build automated ML and ETL pipelines on Azure Databricks; apply NLP (Huggingface, NLTK, Sentence-BERT) and classification methods (XGBoost, neural nets); track experiments with MLflow/Hyperopt; ensure PHI/PII and HIPAA-compliant solutions; collaborate with engineering, product, and clinical teams and document results. Remote position.

The role involves leveraging advanced analytics and machine learning to assess and mitigate credit and fraud risks, developing scalable modeling solutions while collaborating with stakeholders.

Lead development of production-grade fraud and identity risk signals from high-scale device, network, browser, mobile, API, and behavioral telemetry. Define evaluation methods, build scalable feature engineering, investigate adversarial signal patterns, influence telemetry and production readiness, mentor data scientists, and drive technical direction for robust, explainable, low-latency decisioning systems.

Develop ML features, models, and analytical methods from device, network, browser, mobile, session, and behavioral telemetry to detect fraud and improve identity confidence. Handle large-scale, noisy, high-cardinality data, design validations, assess drift and leakage, collaborate with engineering/product/risk for production rollout, document models and features, and mentor junior team members.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Staff Data Scientist will own the analytical strategy for Firefox, driving data-informed decisions through measurement frameworks, experiments, and user growth analysis, while mentoring other data scientists.

The Staff Data Scientist will lead analytical strategies for Firefox, focusing on product growth, measurement frameworks, and mentoring data scientists while guiding data-driven decisions.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

Build, evaluate, and deploy LLM/ML models and synthetic datasets to improve Community Support. Identify opportunities via data exploration, analyze structured and unstructured data, automate LLM evaluation, personalize experiences, and collaborate cross-functionally to drive product and operational impact.