Data Scientist

• Provide technical leadership for machine learning systems powering Target's digital recommendations and personalization experiences
• Identify opportunities to improve guest experiences through recommendation, retrieval, ranking, and personalization solutions at massive scale
• Lead the design, development, evaluation, and deployment of machine learning models influencing product discovery across Target's digital experiences
• Translate ambiguous business challenges into scalable algorithmic solutions that drive measurable guest and business impact
• Drive projects from problem definition through production deployment and measurement
• Balance innovation with operational excellence and long-term maintainability
• Shape the technical direction of Target's recommendation capabilities
• Establish best practices for model development, evaluation, and measurement
• Influence decisions across product, engineering, and data science teams
• Mentor and develop other scientists
• Raise the technical bar across the organization
• Contribute to the growth of Target's data science community through collaboration, thought leadership, and adoption of emerging machine learning techniques and technologies
• Perform modeling and data science, develop highly performant software, elevate Target's culture, and apply retail domain knowledge

Lead Data Scientist – Recommendations, Applied ML, Reinforcement Learning, Contextual Bandit Design

• Design and implement AI and machine learning solutions for complex business problems across Service and Operation analytics
• Use statistical analysis, forecasting, predictive modeling, machine learning, and GenAI
• Translate ambiguous business needs into analytical approaches with business stakeholders
• Frame problems, define success measures, develop hypotheses, and communicate actionable insights and recommendations
• Support the full lifecycle of data science and AI solutions from problem framing through deployment, monitoring, and continuous improvement
• Lead analytics strategy and enhancement of the field-facing recommendation engine
• Partner with product and technology teams on recommendation solutions supporting personalization, prioritization, and business decision-making
• Apply data, model, and AI governance standards, responsible AI practices, validation expectations, and documentation standards
• Monitor ongoing solution performance
• Advance data science best practices and identify scalable and automated solutions
• Mentor peers through knowledge sharing and stay current on emerging AI, machine learning, and analytics techniques
• Deliver analytic strategy that creates measurable business impact

Senior Data Scientist Manager

• Architect and build MHC’s core data platform across ingestion, transformation, storage, and serving layers for batch and streaming workloads
• Model data for analytics and product use cases, creating documented reusable datasets and a trusted semantic layer
• Establish data quality, lineage, observability, and governance practices
• Build pipelines and feature/embedding stores for analytics, ML, and LLM applications
• Turn operational and document data into metrics, signals, predictions, and document understanding
• Build and evaluate models and retrieval systems, including RAG over documents
• Partner with Product to design and ship LLM-powered features from prototype to production with guardrails, evaluation, and cost controls
• Manage, mentor, and grow a team of 2–4 data and AI engineers
• Set technical standards and a pragmatic data and AI roadmap
• Make build-vs-buy decisions and balance speed with maintainability
• Collaborate with Product, Engineering, and business stakeholders
• Review code and designs, prototype difficult problems, and maintain engineering quality
• Deliver a prioritized roadmap and early data-layer wins within 90 days
• Establish a reliable data foundation and first AI/LLM-powered feature within six months
• Build a steady stream of data products and AI features within one year

Lead, Data AI – Engineering

• Perform data analysis on environmental and engineering datasets
• Develop processes that automate data transformation, analysis, and visualization
• Work with artificial intelligence and machine learning initiatives, including deploying agentic systems
• Collaborate with teams to collect requirements and user stories for digital tools and processes
• Participate in process improvements and special projects
• Support data analysis, data infrastructure, artificial intelligence, and automation projects
• Support internal and client-facing initiatives

Data Scientist jobs in Minnesota

Lead Data Scientist – Recommendations, Applied ML, Reinforcement Learning, Contextual Bandit Design

Senior Data Scientist Manager

Lead, Data AI – Engineering

Data Science Intern