Lead Data Scientist - Recommendations (applied ML, Reinforcement Learning, Contextual Bandit Design)

The pay range is $132,000.00 - $238,000.00

Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at

JOIN TARGET AS A LEAD DATA SCIENTIST – RECOMMENDATIONS (RecSys) About Us: Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture.

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. A role with Target Data Sciences means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Every scientist on Target’s Data Sciences team can expect to do modeling and data science, develop software with highly performant code, elevate Target’s culture, and apply retail domain knowledge. As a Lead Data Scientist - Recommendations, you will provide technical leadership for the machine learning systems that power Target's digital recommendations and personalization experiences. Working closely with data scientists, engineers, product managers, and business stakeholders, you will identify opportunities to improve guest experiences through recommendation, retrieval, ranking, and personalization solutions at a massive scale. You will lead the design, development, evaluation, and deployment of machine learning models that influence how millions of guests discover products across Target's digital experiences. Leveraging expertise in machine learning, deep learning, experimentation, and optimization, you will translate ambiguous business challenges into scalable algorithmic solutions that drive measurable guest and business impact. You will be responsible for driving projects from initial problem definition through production deployment and measurement, balancing innovation with operational excellence and long-term maintainability. You will help shape the technical direction of Target's recommendation capabilities, establishing best practices for model development, evaluation, and measurement while influencing decisions across product, engineering, and data science teams. Beyond delivering solutions, you will mentor and develop other scientists, help raise the technical bar across the organization, and contribute to the growth of Target's data science community through collaboration, thought leadership, and the adoption of emerging machine learning techniques and technologies. Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs. About you: PhD or MS in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research, or a related quantitative field with 2+ years of industry experience 5+ years of experience developing machine learning solutions scaling recommendation, personalization, ranking, retrieval, or search machine learning systems Experience with reinforcement learning and/or contextual bandit design, implementation, and evaluation Experience leading the development, evaluation, and deployment of machine learning solutions and partnering with engineering teams to deliver scalable production systems Strong programming skills in Python and SQL; experience with deep learning frameworks such as PyTorch or JAX 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 and using results to inform product and business decisions Demonstrated ability to translate ambiguous business challenges into scalable machine learning solutions Demonstrated ability to influence technical direction and drive alignment across product, engineering, and business stakeholders Experience leveraging modern AI and generative AI tools to accelerate development, experimentation, and model delivery Excellent communication skills with the ability to clearly communicate complex technical concepts to both technical and non-technical audiences Strong software engineering fundamentals, including testing, code reviews, documentation, and maintainable system design This position may be considered for a Remote or Hybrid (known internally at Target as "Flex for Your Day") work arrangement based on Target's needs. A Remote work arrangement means the team member works full-time from home or an alternate location that's not a Target location, does not have a desk at a Target location and may travel to HQ up to 4 times a year. A Hybrid/Flex for Your Day work arrangement means the team member's core role may be performed either remote or onsite at a Target location depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target.

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Americans with Disabilities Act (ADA) In compliance with state and federal laws, Target will make reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please reach out to View email address on click.appcast.io. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed through this channel. Application deadline is : 10/29/2026