Sr Applied Data Scientist - Personalization (applied ML, PyTorch, RecSys)
$98,000–$176,000 year
Hybrid · Brooklyn Park, Minnesota, United States
Job Summary
Senior data scientist focused on personalization and recommendations, building AI-driven predictive models at scale with PyTorch/JAX, SQL and Python. Leads development of RecSys products from data exploration to production deployment, collaborates with ML engineers and product managers, designs experiments (offline and online) and analyzes results to optimize recommendations. Applies deep learning, statistics, and optimization techniques; communicates findings to technical and non-technical audiences; works in a Hybrid/Flex setup at Target’s Minnesota location, contributing to cross-functional teams in digital marketing, supply chain, and personalization efforts.
Required Qualifications
- MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research or relevant work experience
- 3+ years of experience developing machine learning models using deep learning frameworks such as PyTorch or JAX
- Experience building recommendation, personalization, search, ranking or retrieval systems at scale
- Strong programming skills in Python and SQL
- Experience with optimization, statistics, probability and experimental design
- Experience evaluating models through offline analysis on large-scale datasets and online experimentation
- Knowledge of large-scale data processing and analytics platforms such as Spark
- Ability to communicate complex technical concepts to both technical and non-technical audiences
- Hybrid/Flex for Your Day work arrangement and ability to work onsite at Target HQ MN location and virtually
- Self-driven, results-oriented and able to meet tight deadlines
Desired Qualifications
- MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research or relevant work experience
- 3+ years of experience developing machine learning models
- Experience with PyTorch or JAX
- Experience building recommendation/personalization/search/ranking systems at scale
- Strong programming skills in Python and SQL
- Experience with optimization, statistics, probability and experimental design
- Experience evaluating models via offline and online experiments
- Knowledge of Spark or similar large-scale data processing platforms
- Ability to communicate results to technical and non-technical audiences
- Hybrid/Flex work arrangement—able to work both onsite and virtual
- Experience leveraging generative AI tools for development and delivery
- Self-driven, collaborative, and deadline-oriented
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