Senior Data Scientist - Assistant Vice President

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 → productionization → 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

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