Lead Data Scientist, Recommendations & RL (Remote)

Target; Minneapolis (MN); Hybrid; USD 132,000 - 238,000; Full time

Benefits offered by this job

Health benefits

401(k)

Paid time off

Paid holidays

Paid vacation

Employee discount

Job summary

Target's Data Sciences team seeks a Lead Data Scientist for Recommendations. You will lead ML models powering Target's digital recommendations and personalization at scale, collaborating with data scientists, engineers, product managers, and business stakeholders.

You will mentor others and shape technical direction across the organization. You will drive projects from problem definition to production deployment, using reinforcement learning, experimentation, and modern AI techniques to deliver

Qualifications

• PhD or MS in Computer Science, ML, Statistics, OR or related field with 2+ years industry experience.
• 5+ years building ML solutions for recommendations, personalization, ranking, retrieval, or search.
• Experience with reinforcement learning and contextual bandits.
• Proven ability to lead ML projects and partner with engineering teams.

Responsibilities

• Lead design, development, evaluation, deployment of ML models at scale.
• Mentor and develop other scientists; raise technical bar across teams.
• Collaborate with data scientists, engineers, product managers and stakeholders.
• Translate business challenges into scalable machine learning solutions.

Skills

Python

SQL

Communication

Leadership

Experiment design

Software engineering

Reinforcement learning

Online experiments

Education

PhD or MS in CS/ML/Statistics/OR

Tools

PyTorch

JAX

Spark

Job description

Target's Data Sciences team seeks a Lead Data Scientist for Recommendations. You will lead ML models powering Target's digital recommendations and personalization at scale, collaborating with data scientists, engineers, product managers, and business stakeholders.

You will mentor others and shape technical direction across the organization. You will drive projects from problem definition to production deployment, using reinforcement learning, experimentation, and modern AI techniques to deliver