Sri Vasudev at Times Square, New York

Sri
Vasudev

Data, Analytics & AI  ·  Product  ·  Tech & Management Consulting

Turning enterprise data into decisions — across Fortune 500 consulting, public-sector transit, and AI product teams.

Analytics & BI Supply Chain Product Management Consulting & Strategy
Seeking full-time roles  ·  Analytics  ·  Product  ·  Consulting
View My Work Download CV
Los Angeles, California  ·  Open to relocation anywhere in the US
GitHub · LinkedIn · sri.vasudev14@gmail.com
Experience at
EY Ernst & Young
MTA MTA
IpserLab IpserLab
Consilio Consilio
About

At the intersection of
data, AI, and
product strategy.

Data & Analytics Engineer working at the product and consulting edge — 2.5+ years delivering digital transformation, AI-enabled products, and enterprise data solutions. Currently a Product Manager Intern at IpserLab LLC in Los Angeles — leading GenAI/LLM integration strategy and product requirements for an internal platform.

Previously at MTA in New York and Ernst & Young, delivering AI-enabled supply chain analytics across four Fortune 500 clients and lifting demand planning productivity by 25%.

AWS Solutions Architect certified. McKinsey Forward Program graduate. MS in Management Information Systems at Pace University (3.8 GPA).

Analytics & BIData Engineering Azure & AWSAI/ML & GenAI Product ManagementStrategy & Consulting Power BI / TableauSCM
Bengaluru
EY · Consilio
2022 – 2024
New York City
MTA · Pace ’26
2024 – 2026
Los Angeles
IpserLab LLC
2026 – Present
Data and AI visualization
Journey
From India’s Silicon Valley to New York, then LA — chasing the hardest problems at the edge of data and AI.
Started in India’s tech capital. Built enterprise solutions at EY. Navigated New York’s transit data at MTA. Now shaping AI products in Los Angeles.
2.5+
Years
3
Cities
4
Companies
Impact
Enterprise scale, measurable results. Supply chain analytics delivered into four Fortune 500 clients.
Consulting BI at EY. Transit analytics at MTA. Legal data ops at Consilio. Every engagement accountable to numbers.
F500 Clients 4
Demand Planning 25%
Data Quality 60%
Analytics
Technology
Azure. Databricks. GenAI. AWS. Power BI. The full stack of modern enterprise data.
ETL pipelines, cloud data lakes, GenAI workflows, and BI dashboards — built for scale, designed for decisions.
Azure Databricks Power BI GenAI / LLMs AWS
Research & Credentials
Published researcher. McKinsey Forward Program graduate. AWS certified. GPA 3.8 at Pace University.
HCI International 2026 publication on LLMs and personality insights. Capstone on agentic AI trust in digital banking.
AWS
Solutions Architect Associate
M
McKinsey Forward Program
2x
Published Author, 2022–2026
Experience

Where I’ve delivered.

Current Role

Product Manager Intern

Los Angeles, CA ·Aug 2026 – Present
  • Lead product requirements for an internal platform, structuring business goals and engineering constraints into a PRD, user stories, and acceptance criteria anchoring a prioritized Agile backlog and roadmap.
  • Drive GenAI/LLM integration strategy, evaluating use cases against system architecture and user needs, designing the UAT framework that validates each release against stakeholder requirements.
GenAI/LLMsPRD Writing Jira/ConfluenceUAT DesignAgile
IpserLab LLC
IpserLab LLC
Los Angeles · California
GenAI Product Agile

Data Strategy Analyst

Enterprise Asset Management (EAM) · Strategic Initiatives Division
MTA Headquarters, New York, NY ·Jul – Dec 2025
  • Analyzed operational and asset datasets with SQL and AI-assisted analytics; applied GenAI tools for data transformation, improving decision-making accuracy by 20%+.
  • Designed 3+ data models and Power BI dashboards on Azure Data Lake; enhanced GIS pipelines within HxGN EAM, improving real-time asset visibility across enterprise reporting.
  • Streamlined cross-functional governance via Jira & Confluence, improving project alignment by 25%.
Azure Data LakePower BI SQLGIS / HxGN EAM
New York City skyline
New York City

Consulting BI Engineer — Advanced Analyst, Staff II

Data & Analytics · BI · Digital Supply Chain & S&OP · Product Engineering
Bengaluru, India ·Dec 2022 – Aug 2024
  • Contributed to EY ASTERISK — EY’s SaaS supply chain platform of 30+ modules spanning demand planning, order management and procurement. Built product roadmaps and GTM strategies for its AI-enabled SCM solutions across Automotive and Consumer Goods.
  • Managed end-to-end data engineering and analytics workflows in SQL, Python, Power BI and Azure Databricks — cutting processing time 30% and lifting data quality 60%.
  • Delivered five tailored analytics solutions across four Fortune 500 clients, raising supply chain visibility and efficiency 15% and client project productivity 25%.
  • Implemented process enhancements for leading Indian automotive clients — 20% operational efficiency gain and reduced time-to-market.
  • Partnered with C-suite executives, suppliers, direct vendors and operations teams, improving demand planning productivity 25% and optimising Agile sprint planning across SCM.
Azure DatabricksPython / SQL Power BISCM / GTM
Ernst & Young

Data Operations Analyst

Bengaluru, India ·Oct – Dec 2022
  • Designed & implemented cloud-based ETL pipelines for legal eDiscovery forensics, enforcing data privacy and chain-of-custody compliance across 5,000+ legal records under strict regulatory standards.
  • Built scalable data warehousing solutions and automated analytics workflows in Python and SQL, improving pipeline throughput by 20% and accelerating analytical report generation by 30%.
  • Partnered with legal and engineering teams to document data lineage and implement audit trails, reducing manual review cycles and strengthening evidentiary integrity across multi-jurisdiction matters.
Python / SQLETL Pipelines eDiscoveryData Compliance
Consilio LLC
Consilio LLC
Bengaluru, India
eDiscovery Data Legal Tech
Domain Depth

Supply chain, end to end.

Two years at EY on the product team behind ASTERISK, EY’s SCM platform — carrying client engagements from requirements through deployment, then running the planning cycles and the executive reviews that followed. Four Fortune 500 accounts across Automotive and Consumer Goods.

01
Discovery

Requirements gathered with client planning teams and supply chain leads

02
Solution Design

Mapping the client’s planning process onto ASTERISK modules and data

03
Deployment

Configuration, UAT and production release into the client environment

04
Cycle Operations

Running the planning cycle live, with analytics on how it performed

05
Executive Review

Post-cycle readout to supply chain heads and planning managers

Supply Chain Management Consulting

01

Sales & Operations Planning

Ran the cycle end to end on ASTERISK, then chaired the post-cycle executive review — product performance and planning analytics presented to supply chain heads and planning managers.

Cycle operationsExec reviewsScenario planning
02

Demand Forecasting

Anticipating volume from history, seasonality and commercial signal — then measuring accuracy and bias so the plan improves rather than repeats. Lifted demand planning productivity 25% with client planning teams.

Forecast accuracyBias trackingPredictive analytics
03

Supply Planning

Turning a demand plan into procurement and production schedules, with inventory policy balanced against service levels and working capital.

Inventory policyService levelsCapacity
04

Vendor & Supplier Management

Supplier scorecards, KPI tracking and performance monitoring — delivered working directly with suppliers, direct vendors and client operations teams.

Supplier KPIsScorecardsGeo analytics
Where this came from — Ernst & Young, Business Consulting
25%
Demand planning productivity gain, working with C-suite, suppliers and operations
5
Tailored analytics solutions raising supply chain visibility and efficiency 15%
20%
Operational efficiency gain for leading Indian automotive clients
4
Fortune 500 clients across Automotive and Consumer Goods
Technical Skills

The stack I work with.

Analytics & BI
Power BITableauData VisualizationGoogle AnalyticsKPI & Metrics FrameworksPredictive AnalyticsRapidMinerGIS
Strategy & Product
SDLCPDLCBusiness StrategyProduct & Stakeholder ManagementGTM StrategyProduct RoadmappingRequirements GatheringProgram & Project ManagementSCM AnalyticsS&OPDemand ForecastingSupply PlanningVendor & Supplier ManagementProcess MappingAgile / ScrumJira / ConfluenceServiceNowCX
Data & Cloud
ETL / ELT OrchestrationData ModelingAirflowMicrosoft FabricAzure Data LakeAzure DatabricksAWS S3Hadoop / HivePostgreSQLMySQLMicrosoft SQL Server
AI / ML
GenAILLMsAgentic AIRAGPrompt & Context EngineeringAI Adoption & EnablementClaudeOpenAIMicrosoft Copilot
Programming & Core
PythonPandasNumPyPyTorchSQLOSGitDockerMS 365ExcelAccessPowerPoint
Education

Academic foundation.

Pace University campus
Pace University
Seidenberg School · New York
MS, Management Information Systems
Data Science Engineering & Analytics
June 2026 GPA 3.8
Jain University campus
Jain University
School of CS & IT · Bengaluru
BS, Computer Science
Applied CS, Cloud, Data & Analytics
July 2022 GPA 3.64
Fellowship · Certifications · Projects

Selected work.

McKinsey Forward
McKinsey & Company
Leadership Program
McKinsey Forward Program ↗
McKinsey & Company · Dec 2025
AWS
Certification
AWS Solutions Architect Associate ↗
Amazon Web Services · Jan 2026
Publications
Revisiting IBM Watson Personality Insights with LLMs ↗
HCI International 2026
Bipolar Occurrence Prediction ↗
Journal of Emerging Technologies and Innovative Research · 2022
Academic Research
"The Suggestive Banker"
Capstone: Consumer Trust in Agentic AI for Digital Banking
Pace University · 2026
SmartChurn Project Case Study
96.51%Phase 3 accuracy
8 / 2Ratio hit exactly
Expand
Machine Learning · Mar 2025

SmartChurn

When is a churn model actually ready to deploy? Great One Bank weighted confidence stability at 50% and accuracy at just 15%. On blind records the model hit the 80/20 ratio exactly at 96.51% accuracy — but flagged one churner at 0.585 confidence, barely above a coin flip.

RapidMinerCatBoostGradient BoostingTableau
Read the case study
HeartBeacon Project Case Study
0.77Recall on at-risk
319,795CDC records
Expand
Machine Learning · Nov 2024

HeartBeacon

Which patients carry undetected cardiovascular risk? Across 319K CDC records at a ~9% positive rate, Random Forest posted the highest accuracy (0.89) but caught only 20% of at-risk patients. Logistic Regression with resampling reached 0.77 recall at 0.73 accuracy — the model I chose, because a missed case costs more than a false alarm.

PythonScikit-learnSMOTESeaborn
Read the case study
Beyond the Desk

What keeps me energized.

Outside of tech, I’m into athletics, travel, city culture, and cars — things that keep perspective sharp and fuel the curiosity I bring to every problem.

Badminton Cricket Pickleball Tennis

Athleticism

Badminton · Cricket · Pickleball · Tennis

Western Ghats Mountains Karnataka

Travel

Western Ghats · Karnataka · Outdoors

Bengaluru San Francisco New York Los Angeles

Cities & Travel

Bengaluru · New York · San Francisco · LA

Mercedes G-Class Porsche 911

Automotive

Car culture & motorsport

Contact

Let’s talk.

MS graduate (Pace University, June 2026) with 2.5+ years across EY, MTA, Consilio & IpserLab. Seeking full-time roles in Data & Analytics Engineering, Business Intelligence, Product, and Technology & Management Consulting. Based in Los Angeles, open to relocation anywhere in the US.

sri.vasudev14@gmail.com
GitHub LinkedIn Resume PDF ↗