Mehul Fadnavis

Analytics Harness | Product & Dev

Took a self-initiated prototype to a production grade product adopted across Payments Analytics. Leading a team of 8, I own product strategy, architecture and roadmap while remaining the primary developer.

• Problem: Built a harness around the analytics process focusing on user personalisation and agent first ergonomics
• Tooling: Skills first harness with MCP connections to internal tools and custom analytics functions and workflows
• Evals: Implemented LLM-as-a-Judge & manual eval pipelines to iteratively improve agent accuracy
• Ecosystem: Skills marketplace and GitLab Pages publishing turned one-off analyses into reusable assets
• Security: Integrated OAuth to enforce RBAC, ensuring secure access to internal data ecosystems
• Adoption: Leadership demos, org-wide rollout, user onboarding drives, 100+ analyses published in H1

Payments Analytics at Grab (On Platform and Off Platform) | Analytics

• Strategy: Shaped 40% of H2 2025 roadmap via data discovery, aligning lending & merchant teams on key priorities
• Segmentation: Segmented 1.63M consumers across SG/MY/PH into 6 mindsets driving the roadmap
• Commercial: Secured buy-in to revive "Shopping Tile," projecting $150K incremental monthly TPV
• Growth: Implemented A/B tests for multiple payment features across diverse touchpoints in the app
• Innovation: Won Finathon (hackathon) by prototyping a payment feature now scaling to 300k monthly users
• Scale: PIC for 4 key payment flows; engineered Star-schema dashboards handling 100M+ rows to unlock insights
• Tracking: Owned Fintech FY2026 Perfect Journey metric end-to-end reporting to exco: definition, automated RCAs
• Engineering: Created and maintained 15+ data pipelines implementing medallion architecture with 10 silver tables, 5 gold tables used for product metrics reporting, root cause analysis and dashboarding using PowerBI and Superset

GenAI Tooling Strategy | Technical Lead

As part of a merge, drove GAI adoption and ways of working across a 20+ analyst org

• Strategic Assessment: Assessed tooling gaps and capability roadmaps to transition to an AI-enabled analytics org
• Self-Serve Platform: Operationalized Snowflake Cortex with open primitives, for self-serve insights with no lock-in

Card Offers (Consumer Product) | Product & Dev

PM and lead analyst for 0 to 1 product through inception, feasibility, development, awaiting launch

• Role: Acting PM & Lead Dev for 0-to-1 launch, validating feasibility via a self-built React MVP
• Tech: Engineered an LLM-powered pipeline to scrape & normalize unstructured offer data into a unified schema
• Product: Owned 0-to-1 roadmap, scoping critical features to navigate strict resource constraints to launch

Analytics Self-Serve Portal | Dev

• Scale & Impact: Co-developed an Agentic AI-enabled portal now serving 1500+ MAUs across the organization
• Infra: Architected scalable, provider-agnostic backend using Google ADK, Docker, and Kubernetes

Instrumentation Automation using LLMs | Product & Dev

• Recognition: Won Grab CEO Award (<1% of org) leading 20+ analysts to deploy an automated clickstream tool
• Product: Scoped the MVP, defined user journeys, then built the core LLM pipeline to process unstructured data

Machine Learning and Generative AI Initiatives across Grab

• Received Approve 2 rating for a patent filed for SQL optimisation using Large Language Models (LLMs) in Grab
• Chat Data Mining scripts across multiple languages, performed Topic Modeling with LLM interpretation

SQL Preview / VS Code Extension + MCP Server / Published on Open VSX

• Traction: 20K+ installs; a local-first SQL workspace for developers and the agents they work alongside
• Positioning: Local-first and read-only by default, the trust constraints enterprises need to adopt agents
• Scope: Own the full stack solo, product brief, 8-engine support, MCP tool design, docs and release

Consulting & Forward Deployed Data Science

Led commercial strategy for clients in education sector; built internal search tool (Elasticsearch) to unify firm knowledge

Strategic planning for an elite university in Saudi Arabia

• Interviewed 15+ experts across various universities to understand market landscape for various programs
• Benchmarked data across top universities in the country to identify success factors and upcoming trends

Transaction Analytics and Consulting

• Built reusable components using Alteryx and Power BI deployed across various use-cases saving delivery time
• Developed search engine using Elasticsearch utilizing old projects allowing for a firm-wide knowledge base

Client facing role, delivering end-to-end ML and Statistical solutions for consumer product verticals

Airlines Predictive Maintenance

• Developed rare event predictive models for a US Airline to identify delays, cut losses and improve fleet efficiency
• Reduced licensing costs and model turnover time by 50% as a result of migration from SAS to R and Pyspark
• Architected user-friendly modeling interface on Azure, enabling non-technical stakeholders to leverage predictive insights and reducing model deployment time from 5 days to 2 days

Sales Forecasting for a major US dairy manufacturing firm

• Developed end to end ML and statistical models for a dynamically updated forecasting tool spanning multiple products across business verticals using Python, Pyspark and HDFS
• Employed multiple statistical (Holt-Winter, Variable Auto Regressor) and machine learning models (Random Forest, Gradient Boosting, Long-Short Term Memory Neural Networks) and picked best performing models

Insights Platform: Internal Big Data Capability Development

• Liaised with multiple teams as advisor on big data implementation of advanced analytical projects
• Created ETL scripts to extract data from SQL servers, Graphql APIs and Cloud Data Lakes in 5 engagements

Skills

• AI & Agents: MCP, LangGraph, LangChain, Google ADK, RAG, LLM-as-a-Judge evals, LangSmith, OAuth/RBAC
• Product: A/B testing, 0-to-1 discovery, clickstream instrumentation, OKR design, PowerBI, Superset, Tableau
• Languages & Frameworks: Python, SQL, PySpark, R, React/Next.js, FastAPI, Streamlit
• Data & Infra: Presto/Trino, Databricks, Spark, Airflow, dbt, Kafka, Postgres, Docker, Kubernetes, AWS

Education and Certification

• AWS Certified Machine Learning – Specialty (MLS-C01) / AWS (2023)
• Nanodegree in ML Devops Engineer / Udacity (2022)
• Masters in Chemical Engineering / IIT Bombay, Mumbai GPA - 8.78/10 (2018)
• Bachelors in Chemical Engineering / IIT Bombay, Mumbai GPA - 8.78/10 (2018)
• Exchange Student, Chemical Engineering / TU Denmark, Denmark GPA - 9.67/12 (2018)