Staff Data Scientist, Machine Learning - India

Professional Experience: 8+ years across analytics, LLMs, data science, and applied machine learning, with a proven track record of owning business-facing solutions.

Product-Focused ML: Experience developing real-time anomaly detection models and large-scale data processing systems to identify product usage signals and adoption patterns.

Business ML Applications: Ability to solve business problems using predictive and prescriptive modeling, including price sensitivity, churn, revenue forecasting, and marketing mix optimization.

Domain Expertise: Knowledge of identity threat signals (e.g., unusual logins, MFA behavior, authentication velocity) and analyzing deviations from historical user behavior.

AI Execution & Advisory: Expertise in managing AI service costs and relevance, building AI agents for automated analysis, and generating explainable insights within business workflows.

Analytics Foundations: Strong background in segmentation, root cause analysis, funnel optimization, and acquisition quality scoring to drive growth and efficiency.

Thought Leadership: Capability to reason through unfamiliar domains to identify potential signals, features, and modeling approaches independently.

Ecosystem Background: Experience in fast-paced, data-first environments or startups where ML is a core driver of business outcomes.

AI Stack Roadmap: Leading the path from opportunity sizing and prototyping through to evaluation, deployment, and impact measurement.

Technical Execution: Ensuring robust performance, validation, and inference of real-time models using the latest industry improvements.

Standards & Governance: Establishing pragmatic standards for model development, LLM evaluation, observability, security, and cost management.

Capability Mentorship: Coaching analysts and creating reusable tools to raise the collective quality of predictive problem-solving.

Strategic Communication: Translating complex technical work into clear recommendations and trade-offs for senior stakeholders.

Cross-Functional Leadership: Aligning stakeholders with competing priorities and creating clarity in ambiguous settings to influence outcomes.

Expertise in Python, SQL, statistical analysis, and common ML frameworks.

Deep knowledge of MLOps, including feature engineering, deployment, inference, and monitoring.

Proficiency with LLM APIs, RAG, Semantic Layers, Embeddings, and agent orchestration frameworks.

Experience building robust ML pipelines for both real-time and asynchronous predictions.