02 / ABOUT
I LIKE TURNING CHAOS INTO SOMETHING USEFUL
I’m Aamodit Acharya, a Statistics and Computer Science student at the University of Waterloo. I like messy problems, good people, and the point where a bunch of scattered ideas finally starts making sense.
Currently: Applied Scientist at Ramp, building automations for reimbursements.
My story started with arguing with friends about sports: hockey takes, F1 calls, and debates that needed more than vibes. I started pulling data, finding patterns, and using insights to prove them wrong. Somewhere in that chaos, I realized I liked turning messy questions into cleaner answers.
- search systems
- /
- experimentation
- /
- growth analytics
- /
- forecasting
03 / EXPERIENCE
Experience
A timeline of my professional experience in data science, analytics, and machine learning.
WORK
Building automations for reimbursements, turning operational workflows into reliable product systems.
CURRENT FOCUS
DOMAIN
Data Science Intern
@ WealthsimpleBuilt production Marketing Data systems across Braze and Airflow: push-token observability, a daily attribute catalogue, and an AI-assisted DAG debugging agent.
KEY IMPACT
TECH STACK
Data Science Intern
@ MashBuilt conversational search infrastructure. Improved answer relevance by 45% via RAG search pipelines. Expanded multimodal agent coverage by 50% and reduced generation hallucinations by 30%.
KEY IMPACT
TECH STACK
Data Science Intern
@ StatsigOptimized compute query pipelines for high-cardinality logs. Supported 5B+ daily events, cut Spark query times by 82% via HLL++ sketches, and pushed latency down by 25% on Iceberg tables.
KEY IMPACT
TECH STACK
Data Science Intern
@ TD BankDeveloped customer cancellation risk metrics on Hadoop databases using Impala SQL. Rewrote legacy processing scripts to reduce runtime by 97%, and engineered automated web scrapers on AWS EC2.
KEY IMPACT
TECH STACK
Data Science Intern
@ TD BankContainerized forecasting scripts into Docker and Kubernetes microservices. Decreased forecasting process time by 67%. Built advisory utilization models using RidgeCV to boost closing rates by 25%.
KEY IMPACT
TECH STACK
Data Science Intern
@ DesjardinsDeveloped monthly auditing SAS pipelines for fraud discrepancy reporting. Reviewed risk underwriting and pricing logic across 10 distinct variables to ensure alignment between Radar and R modeling frameworks.
KEY IMPACT
TECH STACK
VOLUNTEERING
Python drone navigation, YOLO detection, NumPy, PyTorch.
Financial planning, budgeting, events, and club support.
Robotics curriculum, mentoring, AutoCAD, C++, Python.
CREDENTIALS
Supervised Machine Learning: Regression and Classification — covering Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting.
Comprehensive assessment of probability concepts and their application in actuarial science, covering probability theory, random variables, and distributions.
Assesses understanding of fundamental financial mathematics — calculating values for cash flow streams used in valuing loans and bonds, asset/liability management, and investment income.
04 / SELECTED PROJECTS
Projects
SELECT A SIGNAL / OPEN TO EXPLORE
05 / CONTACT
OPEN CHANNELS