Pranit Kumar

DATA ANALYST / PRODUCT THINKER / BUSINESS ANALYTICS

Pranit
Kumar

I bridge analytics and product strategy — from predicting NFL plays with deep learning to building dashboards that drove $300K in sales. UCLA Anderson MSBA '26.

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0 APPLICATION SALES
0 CYCLE TIME CUT
0 ML ACCURACY
0 RECORDS ANALYZED
Python SQL Tableau PyTorch · ML Figma Azure Snowflake Power BI Agile / Scrum
$1.5M

RFP proposal delivered to senior leadership

35%

Conversion tracking accuracy improvement

40%

Approval cycle time cut via Power BI dashboards

18%

Customer base growth from data-driven algorithm

Top 10

Innovation challenge rank across 25,000 global submissions

1.2M AUD

Revenue added through product expansion at TCS

UCLA Anderson MSBA

Statistical foundations, ML, and prescriptive models for decision-making. Expected Dec 2026.

10+ Years at UCLA, Accenture & TCS

From SAP ABAP automation to competitive benchmarking and AI research — delivered measurable business impact.

NFL Big Data Bowl 2026

Deep learning on 650K+ tracking records to predict play outcomes before they happen.

Competitive Volleyball Player

Teamwork in sports mirrors collaboration in analytics — precision, timing, and trust all matter.

01 About Me

Analyst. Builder.
Storyteller.

I'm a Master of Science in Business Analytics (MSBA) student at UCLA Anderson, passionate about blending data, product thinking, and storytelling to drive impact. I specialize in using analytics and machine learning to solve real-world business problems — from predicting player movements in the NFL Big Data Bowl to optimizing SQL schemas for scalable data systems.

Before UCLA, I worked on projects that spanned business intelligence, product strategy, and data visualization — building tools that turned complex data into actionable insights. My work bridges the gap between data science and product management: identifying the "why" behind data, designing solutions that scale, and communicating results clearly across teams.

I'm currently exploring opportunities where I can apply data-driven decision-making to product development, strategy, or analytics innovation.

02 Expertise

Skills & Tools

Agile / Scrum

Prototyping & Wireframing (Figma)

Journey Mapping

Usability Testing

SDLC / User Stories

Stakeholder Management

Python

Python (Pandas, NumPy, scikit-learn)

R

R (Statistical Modeling)

SQL

SQL / Data Management

Java

Core Java

PyTorch

Machine Learning (Regression, NLP, Clustering)

Statistical Analysis & A/B Testing

Tableau

Tableau

Power BI

Power BI

Snowflake

Snowflake

IBM SPSS

IBM SPSS

JIRA

JIRA / Rally / Confluence

Figma

Figma (UX Prototyping)

MySQL

MySQL

Azure

Microsoft Azure

MongoDB

MongoDB

Certified Business Data Analyst (IIBA)

ECBA – IIBA

CSPO (Certified Scrum Product Owner)

POPM – SAFe Agilist 5.0

03 Journey

Experience

Graduate Research Assistant

Los Angeles, CA

University of California, Los Angeles

Jan 2026 – Present
NLPAIResearchData Analysis
  • AI-Driven Customer Perception Research: Applied NLP, entropy scoring, and AI likelihood scoring to large-scale review datasets. Quantified AI-driven shifts in language authenticity and consumer perception, producing structured research reports delivered to faculty and academic stakeholders.

Business Architecture Associate Manager

Gurugram, India

Accenture

Oct 2022 – Sept 2025
PythonTableauSQLPower BI ExcelPowerPointStakeholder Management
  • Competitive Benchmarking & Go-to-Market Strategy: Developed a benchmarking solution for a travel client by consolidating datasets across 10+ sources in Python and Tableau to build a benchmarking analytics platform. Delivered product positioning recommendations that informed the go-to-market launch, generating $300K in application sales within the first year.
  • Customer Analysis & Executive Reporting: Analyzed 100K+ CRM and behavioral records (SQL, Excel, Tableau, Python) to segment high-value cohorts and identify engagement drop-off points. Delivered executive-level PowerPoint business cases to senior leadership, enabling a redesigned campaign that improved post-implementation conversion tracking accuracy by 35%.
  • RFP & Business Case Development: Led analysis of 150+ applications and co-authored a $1.5M RFP response, including competitive positioning, scope assumptions, and effort models in PowerPoint and Word.
  • Cross-Functional Product Delivery: Served as product liaison across Sales, Operations, and Engineering. Built Power BI KPI dashboards and structured reporting cadences, cutting approval cycle time by 40% and enabling faster product iteration.
  • Team Leadership: Led 17 Business Analysts within Accenture's Center of Excellence (CoE), delivering 120 hours of structured training across 6 business analysis knowledge areas, reducing operational training costs by 12%.

Business Architecture Team Lead

Gurugram, India

Accenture

Oct 2020 – Sept 2022
AWSExcelJIRAConfluence Process ImprovementBRD/FRD
  • Process Improvement & Cycle Time Reduction: Conducted end-to-end gap analysis and risk assessment across 6 workflows. Built Excel models to quantify inefficiencies, delivering process and system improvements that reduced cycle time by 60%.
  • Requirements & Product Roadmap Alignment: Authored BRDs and FRDs for an AWS-based legacy insurance transformation; aligned Product, Engineering, and Operations teams via JIRA and Confluence to ensure delivery milestones were met on schedule.
  • Innovation Challenge: As Team Lead, developed Intelligent Driver System, a kinematics-based premium generation model analyzing driver behavior to dynamically price insurance risk. Idea reached the Quarterfinals, ranking in the Top 10 across 25,000 global submissions.

Information Technology Analyst

Kolkata, India & Melbourne, Australia

Tata Consultancy Services

Nov 2013 – Feb 2019
SAP ABAPSQLExcelPythonAutomation
  • Data-Driven Product Expansion: Analyzed 20K+ consumption records (SQL, Excel, Python), segmented users by behavior, and built a SAP ABAP value-added service algorithm. The product grew the customer base by 18% and added ~1.2M AUD in revenue.
  • Workflow Automation & Revenue Generation: Automated energy overconsumption notification workflows via SAP ABAP and Python, building repeatable reporting frameworks that secured 68,000 AUD in new client engagements over 6 months.

Master of Science in Business Analytics

Los Angeles, CA

UCLA Anderson School of Management

Expected Dec 2026
Machine LearningSQLBusiness Analytics
  • Focused on statistical foundations, data management, and prescriptive models for decision-making.

MBA – Analytics & Marketing

Chennai, India

Great Lakes Institute of Management

Mar 2020
Quantitative AnalysisBusiness Case Studies
  • Graduated Magna Cum Laude, specializing in data-driven business analytics and marketing strategy.

B.Tech – Computer Science & Engineering

Kolkata, West Bengal, India

Maulana Abdul Kalam University of Technology

Mar 2013
Distributed SystemsData StructuresAlgorithms
  • Specialized in building algorithms and distributed systems.

NFL Big Data Bowl 2026 – Prediction Track

Los Angeles, CA

Kaggle Competition | Sports Analytics & Predictive Modeling

2025
650K+ data points
1791 players tracked per frame
Deep Learning (PyTorch)

The NFL Big Data Bowl 2026 challenged data scientists to predict football play outcomes before they occur using Next Gen Stats player-tracking data — transforming high-frequency spatiotemporal coordinates into meaningful predictive features.

Data Engineering

Preprocessed 650K+ tracking records; calculated relative velocity vectors, angular momentum, and inter-player distances to encode player interactions per frame.

Modeling Approach

Implemented deep neural networks in PyTorch with GroupKFold cross-validation to prevent data leakage; compared with gradient boosting baselines.

Evaluation & Insights

Optimized for Kaggle's custom metric using GPU-accelerated training; visualized predicted trajectories and field heatmaps to interpret play outcomes.

Explore on Kaggle

AI-Powered Content Moderation System

UCLA

Machine Learning & Content Safety

March 2026 – Present
92% accuracy (harmful content)
96% accuracy (spam)
<70ms inference

Designed and built an end-to-end ML content moderation pipeline in Python, combining automated classification with a human-in-the-loop review architecture. The system processes user-generated content in real time, flagging harmful material, spam, and threats with high precision while keeping inference latency under 70ms for production-grade responsiveness.

Classification Model

Trained a multi-label text classifier using scikit-learn and NLP feature engineering (TF-IDF, sentiment scores, toxicity signals) across three content categories: harmful, spam, and threat.

Human-in-the-Loop

Built a review queue architecture where low-confidence predictions are routed to human moderators, creating a feedback loop that continuously improves model accuracy over time.

Performance & Scale

Optimized the inference pipeline for sub-70ms latency, enabling real-time moderation at scale. Evaluated precision-recall tradeoffs across thresholds to minimize both false positives and missed threats.

AI-Enabled Feedback & Semantic Search Platform

UCLA

Serverless AI & Vector Search

Jan 2026 – Feb 2026
1K+ records ingested
Sub-100ms latency
Serverless (Cloudflare)

Built a fully serverless semantic search platform using Cloudflare Workers and Vectorize, designed to ingest and query 1K+ customer feedback records in real time. The platform enabled product teams to discover positioning insights through natural language queries, replacing manual keyword searches with AI-driven semantic matching — results were delivered to stakeholders via structured PowerPoint reports.

Serverless Architecture

Deployed on Cloudflare Workers with edge-first design — zero cold starts, globally distributed, and fully managed infrastructure with no servers to provision.

Semantic Search Engine

Used Vectorize to embed and index feedback records as vectors, enabling meaning-based queries like "complaints about onboarding speed" instead of rigid keyword matching.

Stakeholder Delivery

Synthesized search results into structured PowerPoint decks with thematic clusters, sentiment breakdowns, and actionable product positioning recommendations for leadership review.

Food Agent – AI-Powered Restaurant Recommendation System

UCLA

Full-Stack Web Application & AI

2026
Geo-aware search
Natural language queries
Real-time results

Built a full-stack, geo-aware restaurant recommendation system that parses natural language queries like "cheap breakfast open now within 2 miles" and returns nearby options with ratings, distance (Haversine), open/closed status, and direct Google Maps links — powered by the Google Places API.

Frontend

Next.js (React), TypeScript, and Tailwind CSS — responsive, modern UI with browser geolocation integration.

Backend

FastAPI (Python) with Pydantic validation, distance-based ranking via Haversine formula, and dynamic filtering (radius, open now).

APIs & Services

Live restaurant data via Google Places API, browser Geolocation API, and direct navigation links to Google Maps.

Launch Food Agent
04 Writing

Product Management Blog

View all posts
Product Management

Does an MBA make you a good Product Manager?

Exploring why frameworks matter less than applying them — and how structured thinking turns chaos into clarity.

Nov 2025  ·  4 min read
Product Strategy

Product Management Lessons from a Long-Distance Marriage

A humorous take on prioritization, communication, and roadmap alignment — through the lens of marriage.

Oct 2025  ·  3 min read
Discovery

Always Ask: "Why this Feature?"

How focusing on user intent transforms feature requests into impactful product outcomes.

Sept 2025  ·  5 min read
05 Live Demos

Applications & PoVs

View all apps

Employee Skill Gap Analysis

A product PoV demonstrating how AI can instantly generate professional portfolio layouts using structured content inputs — enabling students and job seekers to build automated websites in minutes.

ReactLovable App BuilderOpenAI IntegrationFrontend UI
Launch Demo
⚡

Food Agent – AI Restaurant Finder

A geo-aware, AI-powered restaurant recommendation system that understands natural language queries like "cheap sushi open now within 2 miles" and returns nearby options with ratings, distance, open/closed status, and direct Google Maps navigation.

Next.jsFastAPITypeScriptGoogle Places APITailwind CSSHaversine
Launch Food Agent
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06 Get in Touch

Let's Connect

If you'd like to collaborate, network, or just say hi — I'd love to hear from you.

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