Lead Data Scientist- Comp Intel

Target- Data Science Competitive Intelligence team is offering an exciting opportunity to solve state-of-the-art problems for Competitor Product matching. The team is rapidly growing and creating massive business impact by building cutting edge systems. We use NLP, deep learning, classical machine learning, t ransformers based architectures, and GenAI/Agentic AI (including RAG pipelines, LLM-powered agents, and tool-use frameworks) to build best in-class data products . As we build the future of Competitive Intelligence, we are looking for driven and passionate individuals with deep expertise in developing AI/ML systems at scale and leading high impact charters.

If you are that person, you can expect to be involved in:

Leading the design, development, productionization and ongoing upkeep of AIML systems across Competitive Product Classification, Matching and Validation.

Owning technical direction for a problem area: defining strategy, influencing roadmaps, setting quality bars, and driving execution through a team of scientists and engineers

Architecting end-to-end solutions that integrate AIML modeling, experimentation (offline + online), and engineering systems for scalability, latency, and reliability, including t ransformer based models, embedding systems, and retrieval-augmented generation (RAG) pipelines. Developing a multiyear vision for key ML & AI capabilities Competitive Intelligence, aligned to business outcomes and measurable metrics

Serving as a technical leader and mentor, raising the bar for scientific rigor, design reviews, and best practices across the org anization

We’re looking for strong domain depth and evidence of impact in the following: NLP / Deep Learning / Agentic AI & GenAI / Search & Information retrieval (e-commerce or large-scale Retail or consumer products preferred), including Transformers, semantic search, vector databases, RAG systems, and autonomous/agent-based workflows

4-year degree in a quantitative discipline (Science, Technology, Engineering, Mathematics) or equivalent practical experience

7+ years of professional data science / applied ML experience (or equivalent), with a strong track record of delivering production AIML systems and measurable business impact

Deep expertise in modern ML techniques including deep learning, NLP, GenAI, and Agentic AI approaches (such as Transformers, LLMs, RAG, and multi-agent systems), with strong judgment on when to use simpler methods

Demonstrated ability to lead large, ambiguous problem spaces: framing, solutioning, driving alignment, and delivering through cross-functional partners

Strong hands-on programming skills in Python, SQL, and Spark, plus comfort working closely with engineering stacks for online inference, data pipelines, and model lifecycle tooling on GCP or similar cloud provider.

Experience with LLM adaptation (e.g., fine-tuning, instruction tuning, preference optimization) and/or agentic workflows (tool use, RAG, evaluation harnesses, orchestration, safety/quality guardrails) applied to Product similarity/Classification or similar use-cases, including prompt engineering, context management, and grounding strategies

Strong analytical thinking and applied research skills: ability to build evaluation frameworks, perform error analysis, and iterate based on data and user outcomes

Excellent communication skills: able to influence technical and non-technical stakeholders, write clear RFCs/design docs, and drive decisions in reviews

Self-driven, results-oriented, and able to operate as a multiplier across teams

Publications or accepted papers/posters in industry tracks at top-tier conferences (e.g., SIGIR, KDD, WWW, NeurIPS , ICML, ACL, EMNLP, RecSys ), or equivalent demonstrated external technical contributions (open-source, patents, invited talks)

Experience operating AIML systems at scale: latency/throughput constraints, model monitoring, drift detection, experimentation platforms, and production incident learnings, including LLM system evaluation, retrieval quality metrics, and agent reliability/observabilit y

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