Lead Data Scientist - Advanced AI (applied Ml, Agentic, Gen Ai)

Target Target · Retail · NCD-0375 Brooklyn Park, MN

Lead Data Scientist role focused on building end-to-end AI/ML systems, including agentic architectures and LLM-powered applications, for enterprise business value. Responsibilities include problem framing, model development, evaluation, scaling, and partnering with engineering teams for production deployment, monitoring, and responsible AI practices. Requires strong Python, ML/DL libraries, and experience with LLM concepts like RAG and agentic workflows.

What you'd actually do

  1. help identify, design, develop, evaluate, and scale AI/ML capabilities that drive automation, insight, and action across core business workflows
  2. work closely with AI Engineers, Full-Stack Engineers, product partners, platform teams, security teams, and business stakeholders to translate ambiguous business problems into practical AI/ML solutions
  3. provide hands-on data science leadership across Advanced AI initiatives
  4. frame problems, define success metrics, explore data, develop modeling approaches, design experiments, evaluate model and system performance, and help guide solutions from prototype to production
  5. partner with engineering teams to ensure AI/ML solutions are reliable, measurable, maintainable, and aligned to Target’s enterprise standards

Skills

Required

  • Python
  • Pandas
  • NumPy
  • scikit-learn
  • PyTorch
  • TensorFlow
  • problem framing
  • data exploration
  • feature engineering
  • model selection
  • experimentation
  • validation
  • evaluation
  • deployment partnership
  • monitoring
  • continuous improvement
  • communication skills
  • mentoring

Nice to have

  • LLMs
  • prompt engineering
  • retrieval-augmented generation
  • agentic workflows
  • model APIs
  • embeddings
  • evaluation frameworks
  • AI observability tools

What the JD emphasized

  • production-grade applications
  • agentic architectures
  • LLM-powered applications
  • retrieval-augmented generation
  • agentic systems
  • intelligent automation
  • evaluation strategies
  • model monitoring approaches
  • responsible AI practices

Other signals

  • end-to-end AI/ML systems
  • production-grade applications
  • agentic architectures
  • LLM-powered applications
  • retrieval-augmented generation
  • agentic systems
  • intelligent automation