Lead Data Scientist- Comp Intel

• 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 transformer 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 organization

Preferred Domain Experience

• 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

About You

• 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