Sr Applied Data Scientist - Personalization (applied Ml, Pytorch, Recsys)

Target Target · Retail · NCD-0375 Brooklyn Park, MN

Sr. Applied Data Scientist focused on building and augmenting AI-driven digital Recommendation products at Target. Responsibilities include data exploration, implementing algorithmic solutions, deploying to production, and analyzing performance. Requires experience with deep learning frameworks (PyTorch), recommendation/personalization systems, Python, SQL, and large-scale data platforms like Spark. Experience with generative AI tools is also mentioned.

What you'd actually do

  1. build and augment our AI-driven digital Recommendation products
  2. perform data exploration and analysis
  3. implement algorithmic solutions given specifications
  4. push solutions to our production environment
  5. analyze performance and trade-offs

Skills

Required

  • MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research or relevant work experience
  • 3 plus 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
  • Demonstrated experience with optimization, statistics, probability and experimental design
  • Experience evaluating models through offline analysis on large-scale datasets and online experimentation, including statistical analysis and interpretation of results
  • Knowledge of large-scale data processing and analytics platforms such as Spark
  • Ability to communicate complex technical concepts and results to both technical and non-technical audiences
  • Demonstrated ability to translate business problems into scalable data science solutions

Nice to have

  • Extensive experience leveraging generative AI tools to accelerate development, experimentation and model delivery

What the JD emphasized

  • building recommendation, personalization, search, ranking or retrieval systems at scale
  • Experience evaluating models through offline analysis on large-scale datasets and online experimentation, including statistical analysis and interpretation of results
  • Extensive experience leveraging generative AI tools to accelerate development, experimentation and model delivery

Other signals

  • recommendation systems
  • personalization
  • production deployment
  • large-scale data processing