Resume
Nripesh Pradhan
Head of Risk Intelligence & Automations · Chipper Cash · San Francisco Bay Area
Machine learning engineer with 7+ years designing and shipping production ML and LLM systems for a 10M+ user financial platform across 7+ global markets. I built the risk and compliance stack from scratch, real-time scoring services, LLM decision pipelines with rigorous evals, anomaly detection, and the data infrastructure underneath, operating at staff scope leading a quantitative engineering team.
- 7+years
- building production ML and data systems
- 10M+users
- served by systems I designed and shipped
- $1.4M+/yr
- in fraud losses prevented by one ML model
- 6,000+/day
- fraudulent signups blocked in production
Selected impact
Shipped an LLM decision system with real evals
A two-model LLM pipeline that adjudicates watchlist screening alerts, validated against a double-blind golden dataset before launch, then run over a 148K-alert backlog at four cents a decision with precision and recall tracked in production.
Read the case studyBuilt real-time ML scoring services
A CatBoost model that scores every new user within an hour of signup, blocking 6,000+ fraudulent referrals a day, and a card-deposit scoring service that runs velocity and rolling-aggregate features at sub-5s latency.
Read the case studyAutomated a fraud desk with LLMs
An autonomous pipeline that ingests unstructured partner reports, applies LLM extraction and identity resolution, and executes account actions in under 20 seconds, down from 30 minutes of manual work. Runs 24/7.
Read the case studyRebuilt the data platform
Led company-wide dbt adoption, designed the canonical financial data models 15+ engineers build on, replaced a managed runtime with a self-hosted one, and shipped the monitoring that catches silent failures.
Read the case studyExperience
Oct 2025 - Present
Head of Risk Intelligence & Automations
Chipper Cash
Lead a quantitative engineering team building ML systems, real-time pipelines, and automated decision engines across 7+ global markets.
- Drove the multi-year systems strategy, begun as tech lead, of replacing third-party vendor platforms with purpose-built quantitative systems, cutting $500K+ a year in costs and reducing end-to-end decision latency by over 80%.
- Designed and built a high-throughput processing platform from scratch in Python and PostgreSQL, orchestrating 53,000+ events, 20,000+ investigation workflows, and 900+ automated filings across 8 currencies with deterministic audit trails.
- Architected a real-time signal-detection pipeline evaluating millions of financial transactions against 20+ configurable statistical rule sets, surfacing 2,000+ actionable signals daily at sub-5-minute latency.
- Built a dual-model NLP system for entity resolution and probabilistic match scoring across millions of records, achieving 85% precision and 95% recall against custom-curated international datasets.
- Engineered an autonomous pipeline that ingests unstructured reports via API, applies LLM-based extraction and identity resolution, and executes automated actions in under 20 seconds, down from 30 minutes.
Jan 2022 - Oct 2025
Staff Tech Lead, Risk & Growth
Chipper Cash
Promoted to Staff in role. Designed and shipped ML-powered scoring systems and production services handling real-time financial data.
- Designed and deployed a real-time ML scoring model combining IP geolocation, behavioral timing, device fingerprinting, and identity-graph signals. Blocked 6,000+ bad actors a day, saving $1.4M+ a year.
- Built a real-time ML scoring service for every card deposit, combining velocity features, rolling statistical aggregates, and behavioral signals at sub-5s latency. Intercepts $20K+ a day in losses.
- Deployed a real-time NLP classification service that parses unstructured transaction data, extracting merchant identity, category, and metadata with sub-second latency at production scale.
- Reduced account-takeover events from 30+ a day to under 10 a quarter with a multi-signal authentication system combining biometric verification, device reputation scoring, and behavioral anomaly detection.
- Automated a dispute engine applying rule-based qualification, generating structured evidence packages, and submitting via processor API. Recovered $100K+ in disputed funds.
Feb 2021 - Jan 2022
Senior Data Scientist
Chipper Cash
Built core financial data infrastructure, statistical analysis frameworks, and production monitoring systems.
- Led company-wide dbt adoption and designed the foundational financial data models powering all analytics, dashboards, and internal tools. Established SQL standards and code review across a 15+ person engineering team.
- Conducted quantitative investigations that shaped policy: surfaced referral-abuse patterns during a 50K-a-day user spike, analyzed loss distributions by typology, and identified anomalous return rates that led to new automated controls.
- Built a self-service experimentation platform enabling targeted launches reaching 900K+ users, and production alerting monitoring transaction rates across all payment providers in real time.
Oct 2018 - Feb 2021
Senior Consultant, Data Science
Logic (acquired by Accenture)
Client-facing quantitative consulting for Fortune 500 retail.
- Delivered predictive modeling and quantitative analytics for Best Buy and Kendra Scott, leading engagements end to end: demand forecasting, inventory optimization, and pricing strategy, presented to senior leadership.
Systems shipped
Real-time ML & scoring
CatBoost; IP, device, and identity-graph signals; 6,000+ blocks a day
velocity and rolling aggregates at sub-5s latency
multi-signal authentication; 30+ a day to under 10 a quarter
NLP classification of raw transaction strings, sub-second
LLM systems
two-model LLM pipeline with golden-dataset evals
LLM extraction and identity resolution; 30 minutes to 20 seconds
six standardized signal types from one YAML config
Data platform
the canonical financial models every downstream system is built on
20+ statistical rule sets; 2,000+ signals a day at sub-5-minute latency
config-driven checks with auto-segmented root cause
Platforms & growth
53K+ events and 900+ automated filings with deterministic audit trails
Python + Dask; 900K users per run
re-checks risk flags at the moment money moves
Toolbox
Languages
Python · SQL · TypeScript
ML / AI
LLMs · OpenAI API · CatBoost · scikit-learn · NLP
Data systems
dbt · Snowflake · PostgreSQL · Airflow · Pandas · NumPy
Infrastructure
Docker · GCP · AWS · Flask · Neo4j · Pinecone · Datadog
Education
Grinnell College
B.A. in Mathematics & Statistics
2018
Budapest Semesters in Mathematics
Study abroad in advanced mathematics
Stanford Continuing Studies
Coursework in literature and finance
HBX CORe, Harvard Business School
Business analytics and economics
Beyond the systems work, I write: essays on fintech and risk, the craft of building, and the books I keep turning over.