Lead Data Scientist- Comp Intel

Target Target · Retail · Bangalore, India

Lead Data Scientist for Competitive Intelligence team focused on competitor product matching using NLP, deep learning, transformers, and GenAI/Agentic AI (RAG, LLM agents, tool use). Role involves designing, developing, and productionizing AIML systems, owning technical direction, architecting end-to-end solutions, and mentoring a team. Requires expertise in modern ML, GenAI, Agentic AI, and production AIML systems.

What you'd actually do

  1. Leading the design, development, productionization and ongoing upkeep of AIML systems across Competitive Product Classification, Matching and Validation.
  2. Owning technical direction for a problem area: defining strategy, influencing roadmaps, setting quality bars, and driving execution through a team of scientists and engineers
  3. Architecting end-to-end solutions that integrate AIML modeling, experimentation (offline + online), and engineering systems for scalability, latency, and reliability, including transformer based models, embedding systems, and retrieval-augmented generation (RAG) pipelines.
  4. Developing a multiyear vision for key ML & AI capabilities Competitive Intelligence, aligned to business outcomes and measurable metrics
  5. Serving as a technical leader and mentor, raising the bar for scientific rigor, design reviews, and best practices across the organization

Skills

Required

  • Python
  • SQL
  • Spark
  • deep learning
  • NLP
  • GenAI
  • Agentic AI
  • Transformers
  • LLMs
  • RAG
  • multi-agent systems
  • prompt engineering
  • context management
  • grounding strategies
  • LLM adaptation (fine-tuning, instruction tuning, preference optimization)
  • agentic workflows (tool use, RAG, evaluation harnesses, orchestration, safety/quality guardrails)
  • building evaluation frameworks
  • error analysis

Nice to have

  • Publications or accepted papers/posters in industry tracks at top-tier conferences
  • open-source contributions
  • patents
  • invited talks
  • model monitoring
  • drift detection
  • experimentation platforms
  • production incident learnings
  • LLM system evaluation
  • retrieval quality metrics
  • agent reliability/observability

What the JD emphasized

  • state-of-the-art problems
  • building cutting edge systems
  • deep expertise in developing AI/ML systems at scale
  • leading high impact charters
  • Architecting end-to-end solutions
  • transformer based models
  • embedding systems
  • retrieval-augmented generation (RAG) pipelines
  • multiyear vision for key ML & AI capabilities
  • technical leader
  • mentor
  • scientific rigor
  • design reviews
  • best practices
  • Deep expertise in modern ML techniques
  • GenAI
  • Agentic AI approaches
  • Transformers
  • LLMs
  • RAG
  • multi-agent systems
  • lead large, ambiguous problem spaces
  • framing
  • solutioning
  • driving alignment
  • delivering through cross-functional partners
  • Strong hands-on programming skills
  • Python
  • SQL
  • Spark
  • working closely with engineering stacks
  • online inference
  • data pipelines
  • model lifecycle tooling
  • GCP
  • LLM adaptation
  • fine-tuning
  • instruction tuning
  • preference optimization
  • agentic workflows
  • tool use
  • RAG
  • evaluation harnesses
  • orchestration
  • safety/quality guardrails
  • Product similarity/Classification
  • prompt engineering
  • context management
  • grounding strategies
  • analytical thinking
  • applied research skills
  • build evaluation frameworks
  • perform error analysis
  • iterate based on data and user outcomes
  • Excellent communication skills
  • influence technical and non-technical stakeholders
  • write clear RFCs/design docs
  • drive decisions in reviews
  • Self-driven
  • results-oriented
  • operate as a multiplier across teams

Other signals

  • building cutting edge systems
  • developing AI/ML systems at scale
  • leading high impact charters
  • architecting end-to-end solutions
  • developing a multiyear vision for key ML & AI capabilities