Lead Applied Data Scientist - Digital Item Recsys (applied Ml, Deep Learning)

Target Target · Retail · Brooklyn Park, MN +1

Lead Data Scientist focused on building and scaling machine learning systems for digital item recommendations and personalization at Target. The role involves technical leadership, model development, evaluation, deployment, and mentoring, with a focus on driving guest and business impact through algorithmic solutions.

What you'd actually do

  1. Provide technical leadership for the machine learning systems that power Target's digital recommendations and personalization experiences.
  2. Identify opportunities to improve guest experiences through recommendation, retrieval, ranking, and personalization solutions at massive scale.
  3. Lead the design, development, evaluation, and deployment of machine learning models that influence how millions of guests discover products across Target's digital experiences.
  4. Translate ambiguous business challenges into scalable algorithmic solutions that drive measurable guest and business impact.
  5. Mentor and develop other scientists, help raise the technical bar across the organization, and contribute to the growth of Target's data science community.

Skills

Required

  • MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research or relevant industry experience
  • 5 plus years of experience leading the development, evaluation and deployment of machine learning (ML) solutions
  • Experience building and scaling recommendation, personalization, ranking, retrieval, or search machine learning systems
  • Strong programming skills in Python and SQL
  • Experience with deep learning frameworks such as PyTorch or JAX
  • Experience leveraging modern AI and Generative AI tools to accelerate development, experimentation and model delivery
  • Experience working with large-scale data processing and analytics platforms such as Spark or equivalent
  • Deep understanding of machine learning, deep learning, optimization, statistics, probability and experimental design
  • Experience designing, analyzing, and interpreting online experiments
  • Ability to translate ambiguous business challenges into scalable machine learning solutions
  • Ability to influence technical direction and drive alignment
  • Excellent communication skills
  • Ability to mentor applied data scientist and help establish best practices for AI/ML and cloud-native development

What the JD emphasized

  • 5 plus years of experience leading the development, evaluation and deployment of machine learning (ML) solutions while partnering with engineering teams to deliver scalable production systems
  • Demonstrated experience building and scaling recommendation, personalization, ranking, retrieval, or search machine learning systems
  • Deep understanding of machine learning, deep learning, optimization, statistics, probability and experimental design
  • Experience designing, analyzing, and interpreting online experiments and using results to inform product and business decisions
  • Ability to influence technical direction and drive alignment across product, engineering and business stakeholders

Other signals

  • recommendation systems
  • personalization
  • retrieval
  • ranking
  • deep learning
  • large-scale data