Jobs / United States / Target Corporation
Lead Applied Data Scientist - Recommendations (Contextual Bandits, Reinforcement Learning)
Target Corporation · 🇺🇸 1000 Nicollet Mall, Minneapolis,MN 55403-2542
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 107 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Target Corporation paid sponsored hires in similar roles23 certified filings for “Senior Data Scientist” (Data Scientists) in MN: $120k–$142k, median $120k. Most were filed at wage level III (36%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
Or apply yourself on the official page →
Sponsor Radar — Target Corporation
The US Department of Labor certified 107 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Target Corporation →
About the role
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 https://corporate.target.com/careers/benefits . 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. Learn more about Target here . 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 machi