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Senior Data Scientist – Assistant Vice President at State Street, Bangalore
Lead ML & Generative AI initiatives, oversee end-to-end data science strategy, and guide production of LLM solutions.
Job Title
AVP - Lead Data Scientist (Overall 12+ years of professional experience)
Role Description
We are seeking an AVP – Lead Data Scientist with deep expertise in applied machine learning and Generative AI/LLM solutions , strong Python skills, and a track record of delivering models and experimentation into production-grade applications/platforms. This role will lead the data science strategy, modeling approach, evaluation standards, and experimentation discipline for GenAI and ML use cases, partnering closely with AI/ML engineers, data engineering, and application/platform teams (including agentic application development). Candidates with experience shaping end-to-end solutions (problem framing → modeling → evaluation → productization → monitoring) and influencing architecture/design decisions are preferred.
Roles & Responsibilities
- Lead end-to-end data science delivery across multiple initiatives: problem framing, hypothesis design, feature strategy, model development, evaluation, and iteration .
- Drive development and improvement of ML and GenAI solutions , including model selection, fine-tuning approaches (where applicable), and performance optimization.
- Define and implement evaluation frameworks for ML/LLM systems (offline metrics, test sets, error taxonomy, regression testing, human evaluation loops).
- Provide DS leadership for RAG-based solutions (retrieval strategy, grounding, relevance quality, context optimization) and partner with engineering on implementation.
- Guide data science contributions to agentic applications : tool selection logic, planning/routing heuristics, guardrails, and measurable quality outcomes.
- Partner with AI/ML engineers and software/platform teams to transition models into production: packaging, APIs, monitoring, and continuous improvement.
- Collaborate with data engineers to define data requirements, data quality checks, preprocessing utilities, and feature engineering pipelines.
- Use cloud platforms (Azure preferred / AWS) to run experiments and support scalable training/inference using managed services where appropriate.
- Establish best practices for experiment tracking, reproducibility, documentation, and model governance.
- Communicate insights and recommendations to stakeholders; translate business goals into measurable ML/GenAI objectives and technical plans.
- Mentor senior/junior data scientists; lead reviews for modeling approach, evaluation design, and experiment results.
- Stay current on ML/GenAI advancements and recommend pragmatic adoption aligned with business and platform constraints.
Core/Must have skills
- BS or Master’s in Computer Science, Computer Information Systems, Engineering, Mathematics, or related field.
- 12+ years overall experience with 8+ years hands‑on Python for applied data science/ML development.
- Strong knowledge of machine learning algorithms , model development lifecycle, feature engineering, and evaluation methodologies.
- Practical experience with LLMs/GenAI (e.g., GPT/BERT-class models), including prompt-based approaches and/or fine-tuning concepts.
- Strong understanding of LLM evaluation and quality measurement; ability to set up repeatable experimentation and regression testing.
- Experience partnering with engineering teams to productionize models (deployment patterns, monitoring, iterative improvements).
- Hands‑on exposure to Azure or AWS ML/AI services and tooling.
- Excellent stakeholder management, communication, and cross‑functional collaboration skills.
Good to have skills
- Experience with prompt engineering , prompt routing strategies, and guardrails for enterprise LLM applications.
- Experience with Databricks and/or Azure Machine Learning for scalable experimentation and pipelines.
- Knowledge of agent frameworks/patterns (e.g., orchestration, tool/function calling, memory) and how to measure agent quality.
- Familiarity with Docker/containerization and basic deployment concepts for DS assets.
- Experience with model risk, compliance considerations, and privacy/security patterns for GenAI (PII handling, prompt injection awareness).
Work Schedule
On-premise
Keywords (If any)
Lead Data Scientist, Generative AI, LLM, Applied Machine Learning, RAG, Agentic Applications, Python, Azure, Model Evaluation, Experimentation
About State Street
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We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.
Equal Opportunity
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
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