{"id":52645,"url":"https://alion.io/job/target-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops","title":"Sr Applied Data Scientist - Personalization (applied ML, PyTorch, ML Ops)","company":{"id":3684,"name":"Target","domain":"target.com","url":"https://alion.io/company/target","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":97,"open_postings":290,"ghost_share":0.024,"stale_share":0.241,"repost_share":0.045,"time_to_fill_p50_days":8,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Brooklyn Park, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":98000,"max":176000,"currency":"USD","period":"year","gross":null,"usd_annual":176000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-08-12T00:00:00Z","employer_posted_date":"2026-08-28","last_verified_at":"2026-10-27T23:31:27Z","board_verified":true,"closed_at":null,"days_open":76,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":76},"description":"The pay range is $98,000.00 - $176,000.00Pay 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.\nJOIN TARGET AS A SR DATA SCIENTIST - RECOMMENDATIONS (RecSys)\nAbout Us:\nWorking 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.\nA 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. Whether you join our Applied Data Sciences or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Digital Marketing, Supply Chain Optimization, Advanced AI, Search and Personalization/RecSys rely on.\nAs a Sr Data Scientist, you’llwork on the Target Data Science Recommendations team collaborating with data scientists, machine learning engineersand product managers to build and augment our AI-driven digital Recommendation products. Through your understanding of deep learning, machine learning, linear algebra, probability theory, statistics, and optimization you’llperform data exploration and analysis, implement algorithmic solutions given specifications, push solutions to our production environment, and analyze performance and trade-offs. We will expectyou to understand Agile principles, follow best-practice software design, participatein code reviews, createa maintainable and well-tested codebase with relevant documentation. On the business side, you’lldocument and present work to technical and non-technical peers, build knowledge on business priorities and strategic goals and leveragethis knowledge while building requirements and solutions for each business need.\nCoreresponsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.\nAboutyou:\nMS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research or relevant work experience \n3 plus years of experience developing machine learning models using deep learning frameworks such as PyTorchor JAX\nExperience building recommendation, personalization, search, ranking or retrieval systems at scale\nStrong programming skills in Python and SQL\nDemonstrated experience with optimization, statistics, probability and experimental design\nExperience evaluating models through offline analysis on large-scale datasets and online experimentation, including statistical analysis and interpretation of results\nKnowledge of large-scale data processing and analytics platforms such as Spark\nExtensive experience leveraginggenerative AI tools to accelerate development, experimentation and model delivery \nAbility to communicate complex technical concepts and results to both technical and non-technical audiences\nDemonstrated ability to translate business problems into scalable data science solutions\nSelf-driven, results-oriented and able to meet tight deadlines \nMotivated team player who thrives in a collaborative - global environment\nThis position will operateas a Hybrid/Flex for Your Day work arrangement based on Target’s needs. A Hybrid/Flex for Your Day work arrangement means the team member’s core role will need to be performed both onsite at the Target HQ MN location the role is assigned to and virtually, 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. Click here if you are curious to learn more about Minnesota.\nBenefits Eligibility\nPlease paste this url into your preferred browser to learn about benefits eligibility for this role: https://tgt.biz/BenefitsForYou_DAmericans with Disabilities Act (ADA)\nIn 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 -accommodation-relatedrequests, such as application follow-ups or technical issues, will not be addressed through this channel.\nApplication deadline is : 10/08/2026","description_format":"text","description_chars":5092,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Life insurance"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","E-commerce Platforms","Online Groceries"],"lifecycle":[{"event":"open","at":"2026-08-12T00:00:00Z"}],"visa":[],"liveness":{"score":5,"band":"cold","label":"Long shot","p_open":0.9,"p_active":0.21,"p_room":0.28,"age_days":76,"expected_fill_days":8,"reasons":["conf:73","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-27T05:46:45Z"},"pay":{"stated_usd_annual":176000,"is_top_pay":true},"html_url":"https://alion.io/job/target-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops","json_url":"https://alion.io/job/target-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops.json","meta":{"generated_at":"2026-10-27T23:50:24Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":16031,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-28T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3684},"rest":"https://alion.io/mcp/rest/get_company?id=3684"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ftarget-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ftarget-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ftarget-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/target-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ftarget-sr-applied-data-scientist-personalization-applied-ml-pytorch-ml-ops"}]}