Role Summary

As a Lead Data Scientist, you will design, build, and scale data science solutions that drive measurable business impact. You’ll lead model development and deployment across modern cloud ML platforms (e.g., Amazon Bedrock, Google Vertex AI, Azure OpenAI), integrate advanced analytics into products and workflows, and mentor a high-performing team. Your work spans statistical modeling, ML engineering, and stakeholder collaboration to turn large datasets into actionable insights and production-grade systems.

What You Will Do

• Deliver generative AI features: design, fine‑tune, and evaluate LLM/LMM applications (prompting, RAG, safety, and evals) that ship to production.
• Build agentic AI workflows: plan and execute multi‑step tasks with tool use, memory, and orchestration; integrate with internal APIs and data sources.
• Build & ship models: Develop, deploy, and scale machine learning models; own full lifecycle from experimentation to production, monitoring, and iteration.
• Integrate analytics: Embed advanced analytics into applications and systems to enhance functionality and decision-making.
• Deep data analysis: Analyze large, complex datasets to extract insights that inform strategy and product direction.
• Raise the bar on rigor: Apply regression, tree-based methods (Random Forest, Boosting), text mining/NLP, neural networks, and clustering with strong statistical validation.
• Optimize pipelines: Improve performance and reliability of ML pipelines and cloud integrations across AWS, Google Cloud, and Azure.
• Mentor & lead: Guide junior teammates; enforce best practices for experimentation, documentation, and reproducibility.
• Partner cross-functionally: Work closely with product, engineering, and business stakeholders to align initiatives with outcomes and timelines.

Requirements

Technical Skills – Must Have

• Generative AI: hands‑on experience with LLMs/LMMs, prompt engineering, retrieval‑augmented generation (RAG), and offline/online evaluation frameworks.
• Agentic AI: experience building agentic systems (task decomposition, tool calling/orchestration, memory/planning) using modern libraries/services (e.g., LangChain, LlamaIndex, or function/tool calling on Amazon Bedrock, Vertex AI, Azure OpenAI) or equivalent.
• Programming & Data: Proficient in Python and SQL; strong applied statistics and data mining.
• ML Techniques: Regression, Random Forest, Boosting, text mining/NLP, neural networks, clustering.
• Frameworks: Hands-on with scikit-learn and TensorFlow for model development.
• Platforms: Skilled in deploying/scaling models using Amazon Bedrock, Google Vertex AI, Azure OpenAI (or equivalent).
• Search & Analytics: Proficient with Elasticsearch for search/analytics use cases.
• Cloud & Pipelines: Demonstrated ability to optimize ML pipelines and integrations across AWS, GCP, and Azure.
• Quality & Communication: Strong analytical/problem-solving skills; excellent written and verbal communication.

Technical Skills – Good to Have

• Vector databases and embeddings (e.g., FAISS, Pinecone, Elasticsearch k‑NN) plus LLM observability/guardrails (safety filters, toxicity checks, output validation).
• Frontend for DS tooling: Angular and JavaScript to build lightweight internal tools/demos.
• Broader DS/DE ecosystem: Familiarity with complementary libraries/services for monitoring, data quality, and lineage.

Qualifications

• Proven experience delivering production generative/agentic AI solutions with measurable impact on user outcomes or team productivity.
• Education: Bachelor’s or Master’s in Mathematics, Computer Science, Statistics, Data Science, or related field.
• Experience: 8+ years as a Data Scientist or Data Engineer with demonstrated leadership responsibilities.
• Leadership: Proven ability to mentor junior team members and collaborate effectively across teams.
• Impact: Track record of influencing delivery quality, reliability, and security of ML solutions and communicating outcomes clearly to stakeholders.

How We Work

• Experimentation & releases: Agile cadence with disciplined experimentation, rollout/rollback, and verification as part of “done.”
• Tool equivalency: Platform names signal a modern environment, but equivalent tools are welcome—we value capabilities and outcomes.
• Culture: Outcome-focused, ownership-driven, proactive communication, and “no-surprises” execution.

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