About
Akash Sharma
AI Engineer · Applied ML · Production Systems
I turn messy data into decisions companies can bet on — end-to-end, in production.
I build production AI systems that convert complex, high-dimensional data into reliable business decisions — forecasting, attribution, experimentation, and ML infrastructure that companies can depend on.
Currently Exploring
Career Narrative
My strongest story is not “I trained models.” It is: I build production AI systems that convert complex data into decisions — ones companies can act on at scale.
Starting in April 2022, I joined a team building an enterprise SaaS intelligence platform as a Data Scientist. Over 2.5 years I built foundational ML infrastructure from scratch — a pipeline projection engine, propensity models, multi-touch attribution, marketing mix modeling, statistical incrementality testing, and the first version of a macro forecast model.
I took on progressively more complex system ownership — a complete rewrite of the outlier handling system, a parallelized Hill Curve Transformer scaling to 1000+ features, cascade bug fixes in scenario planning, and a firmographic-enriched attribution layer.
In December 2024, I transitioned into Revsure AI as an AI Engineer, continuing to own and evolve the same platform — reducing forecast MAPE by ~52%, shipping explainability infrastructure, building a configurable multi-model framework, and resolving production-critical edge cases at scale.
Experience
AI Engineer
CurrentRevsure AI
Dec 2024 – Present
1.5+ years
Continued ownership and evolution of the same B2B SaaS Revenue Intelligence Platform — enhancing the forecasting, attribution, and marketing mix modeling systems built during the prior engagement.
- Reduced booking model MAPE by ~52% through forecast category feature engineering
- Deployed time-decay and average-index forecast adjustment layers to production
- Built dual-algorithm explainability infrastructure (SHAP + coefficient contribution) with BigQuery logging
- Improved configurable multi-model ML framework to support 30+ parameters
- Fixed critical MMX production bugs: MinMax extrapolation, scenario cascade drift, STL edge cases
- Authored comprehensive methodology documentation and customer-facing guidance
Data Scientist
ADA Asia
Apr 2022 – Dec 2024
~2 years 9 months
Built and maintained the ML layer of a B2B SaaS Revenue Intelligence Platform from early prototype through full production — across 25+ models spanning forecasting, attribution, propensity, and data engineering.
- Built the Revenue Forecasting Platform from QTD heuristic through production XGBoost-based EOQ system
- Engineered the Marketing Mix Modeling Platform from research through production
- Designed and owned the Pipeline Projection Engine integrating 8+ ML model families
- Built Markov Chain Multi-Touch Attribution system with firmographic and campaign metadata enrichment
- Migrated revenue metrics pipeline from pandas to distributed PySpark, resolving critical implementation bugs
- Reduced modeling pipeline from 5–6 hours to 1 hour via joblib parallelization
- Provided guidance and mentorship to interns across the team
Education
Post Graduate Program in Data Analytics
Imarticus Learning
Jan 2022 · Thane, India
Bachelor of Information Technology
T.Z.A.S.P. Pragati College
Nov 2020 · Dombivli, India
Verified Credentials
11 active IBM × Coursera credentials verified via Credly (earned 2021).
IBM Data Science Professional Certificate
IBM × Coursera · Oct 2021
Applied Data Science Capstone
IBM × Coursera · Oct 2021
Machine Learning with Python
IBM × Coursera · Sep 2021
Data Analysis with Python
IBM × Coursera · Jun 2021
Data Visualization with Python
IBM × Coursera · Sep 2021
Databases and SQL for Data Science
IBM × Coursera · Apr 2021