Data Scientist III (Telangana)

Role Summary: Senior technical lead delivering enterprise GenAI and advanced ML solutions across BMS functions. Designs/implements LLM applications (RAG, fine-tuning, agentic workflows) and production LLMOps/MLOps practices. Influences technical standards, mentors DS-1/DS-2 talent, and aligns stakeholders from problem framing to delivery. Typical profile: 5+ years applied DS/ML with robust GenAI depth; biopharma/healthcare domain experience preferred. Key Responsibilities: GenAI Architecture Solution Leadership - Lead GenAI solution design: Architect enterprise LLM apps (RAG, agents, automation) from prototype to production. - Fine-tune adapt models: Apply LoRA/QLoRA/PEFT (and alignment where applicable) for domain use cases. - Agentic workflows: Design multi-step orchestration using frameworks such as LangGraph/AutoGen/CrewAI. - Evaluation quality: Own LLM evaluation, hallucination mitigation, and responsible AI standards (e. g. , RAGAS/TruLens/DeepEval). - Knowledge infrastructure: Govern vector DB/search patterns and knowledge integrations for scalable retrieval. Advanced ML, Modeling Statistical Expertise - Own the model lifecycle: Frame problems, define data strategy, build models, deploy, monitor, and iterate. - Advanced modeling: Apply ML/NLP/time series/causal and related methods to ambiguous, highimpact problems. - Governance: Lead documentation, validation, and risk controls aligned to responsible AI and regulated needs (e. g. , GxP). - Experimentation: Design studies (A/B, quasi-experimental) to drive evidence-based decisions. Data Strategy, Engineering Platform Collaboration - Data strategy: Define acquisition, preprocessing, and enrichment for structured/unstructured enterprise data. - EDA insights: Surface patterns and opportunities from multi-domain datasets. - Pipelines: Co-design scalable data/ML pipelines with engineering/platform teams. Technical Leadership Team Development - Technical direction: Drive architecture, standards, and engineering best practices across the pod. - Mentorship: Coach DS-1/DS-2 via reviews, pairing, and growth feedback. - Reusable assets: Build/maintain shared frameworks, accelerators, and templates. - Knowledge sharing: Lead workshops, documentation, and community-of-practice efforts. Stakeholder Engagement Executive Communication - Stakeholder partnership: Align senior leaders on priorities, value, and adoption - Problem framing: Translate ambiguity into scoped workstreams with KPIs and milestones. - Executive communication: Present architectures, results, and recommendations clearly. - Delivery leadership: Manage priorities/risks across multiple initiatives in a matrixed environment. Skills Competencies: GenAI Advanced AI Expertise - LLMs: Production experience deploying and integrating foundation models. - RAG: Design and optimize retrieval (hybrid search, reranking, context strategies). - Fine-tuning/alignment: Apply LoRA/QLoRA/PEFT and related techniques. - Agents: Build orchestrated workflows using common agent frameworks. - Responsible AI: Bias/safety controls, evaluation, and governance in regulated settings. Core Data Science, ML Statistical Skills - Programming: Advanced Python; familiarity with distributed tooling as needed. - Statistics: Inference, Bayesian methods, experimentation, and causal thinking. - ML breadth: Supervised/unsupervised methods; strong model selection and tuning skills. - Data at scale: Strong SQL and experience working with large datasets/cloud data services. Cloud, LLMOps Engineering Excellence - Cloud: Deploy ML/GenAI solutions on AWS or Azure in production. - LLMOps/MLOps: Versioning, CI/CD, monitoring, and safe rollout patterns. - Containers: Docker/Kubernetes for scalable workloads. - Engineering: Git/SDLC, APIs, and maintainable production code. Leadership, Communication Strategic Thinking - Communication: Explain complex AI tradeoffs to senior audiences. - Strategy: Turn ambiguity into roadmaps with measurable outcomes. - Leadership: Set quality bars and drive architectural decisions. Experience Qualifications: - Education: MS/PhD in a quantitative discipline (PhD preferred). - Research/applied work: Evidence of solid ML/AI/NLP/statistical project experience. - Experience: 5+ years delivering production DS/ML solutions end-to-end. - GenAI depth: Hands-on LLMs, RAG, fine-tuning, and agentic workflows (core requirement). - Technical leadership: Lead workstreams, mentor others, and make design decisions. - Advanced ML: Build/validate/deploy complex models with measurable impact. - Cross-functional delivery: Partner across business, engineering, and domain teams to ship. Good to Have: - Thought leadership: Publications, talks, or open-source in ML/GenAI. - Multi-modal exposure: Experience with vision-language or related models. - Knowledge graphs: Familiarity with biomedical ontologies/graphs is a plus. - Commercial analytics: Exposure to .