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.
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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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