Lead Data Scientist-item Science

Target Target · Retail · Bangalore, India

Lead Data Scientist focused on building AI/ML capabilities for item data in the retail ecosystem, including catalog management, quality, enrichment, and discovery. The role involves developing and productionizing solutions using GenAI, LLMs, computer vision, classical ML, deep learning, and agentic AI workflows.

What you'd actually do

  1. Contribute to the research, design, and implementation of leading-edge generative AI technologies to address and solve complex problems that contribute to finding solutions for the retail world.
  2. Work with Multimodal LLMs, enhance model performance, fine-tune LLMs
  3. Work with the team to build and maintain complex software systems and tools.
  4. Adopt modular architecture and good software development/engineering practices to enhance the overall product performance and guide other team members.
  5. Produce clean, efficient code based on specifications.

Skills

Required

  • Python
  • SQL
  • Hadoop/Hive
  • GenAI/LLMs
  • Deep Learning
  • PyTorch or TensorFlow
  • analytical thinking
  • problem solving
  • software development/engineering practices

Nice to have

  • Docker/Kubernetes
  • Prompt Engineering
  • Embedding Generation
  • Retrieval Augmented Generation (RAG)
  • Vector DB
  • Elastic Search
  • Computer Vision
  • Mathematical and statistical concepts
  • optimization
  • data structures and algorithms
  • data analysis
  • visualizations

What the JD emphasized

  • production systems experience
  • item intelligence platforms
  • GenAI, LLMs, computer vision, classical ML, deep learning, and agentic AI workflows
  • productionize groundbreaking GenAI based solutions
  • multimodal LLMs
  • fine-tune LLMs
  • complex software systems and tools
  • modular architecture
  • good software development/engineering practices
  • clean, efficient code
  • programming skills in Python, SQL, Hadoop/Hive
  • programmatic use of GenAI/LLMs
  • Deep Learning based solutions
  • Prompt Engineering, Embedding Generation, Retrieval Augmented Generation(RAG) space (e.g., Vector DB, Elastic Search)
  • Computer Vision & Deep Learning Methods

Other signals

  • building intelligent capabilities
  • item intelligence platforms
  • GenAI, LLMs, computer vision, classical ML, deep learning, and agentic AI workflows