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Hi, It's Aamodit Acharya

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Glad you stumbled upon my webpage! Lets connect!

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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 doing: incoming Data Science Intern at Wealthsimple, working with marketing data to better understand growth and customer behavior.

My data science 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.

40.7128° N, 74.0060° W / 43.6532° N, 79.3832° W

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AAMODIT . ACHARYA

CURRENTLY INDEXING:

search systems
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experimentation
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growth analytics
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forecasting

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03 / EXPERIENCE

Experience

A timeline of my professional experience in data science, analytics, and machine learning.

WORK

ACTIVE ROLE

Data Science Intern

@ Wealthsimple

Jan 2026 to Apr 2026 — Toronto, Ontario

Incoming at Wealthsimple on the Marketing Data team. Working with analytics to support growth and understand customer behavior.

KEY IMPACT

Marketing analytics

Growth insights

Customer behavior analysis

TECH STACK

Marketing Analytics Growth Customer Behavior SQL Python

Data Science Intern

@ Mash

Sept 2025 to Dec 2025 — Toronto, Ontario

Built 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

Better answer relevance (45%)

Multimodal search coverage (+50%)

Lower hallucination rate (-30%)

TECH STACK

RAG Weaviate LLM APIs Cohere Ragas Langfuse Slack Agent

Data Science Intern

@ Statsig

May 2025 to Aug 2025 — Bellevue, WA

Optimized 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

Scalable experimentation metrics

Faster segmentation (25% latency cut)

Reliable high-cardinality modeling

TECH STACK

PySpark HLL++ BigQuery Dagster Iceberg GCP dbt Spark SQL

Data Science Intern

@ TD Bank

Jan 2024 to Apr 2024 — Montreal, QC

Developed 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

Customer retention signal (+22% retention)

Faster data refresh (97% speedup)

Underwriting market signal collection

TECH STACK

Impala SQL Hadoop Python AWS EC2 Selenium Pandas Retention

Data Science Intern

@ TD Bank

May 2023 to Aug 2023 — Toronto, ON

Containerized 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

Faster forecasting cycles (67% speedup)

Reliable containerized modeling pipelines

25% boost in closing-rate predictions

TECH STACK

Python Pandas NumPy Docker Kubernetes AWS EC2 scikit-learn

Data Science Intern

@ Desjardins

Sept 2022 to Dec 2022 — Toronto, ON

Developed 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

Fraud discrepancy reporting (30% accuracy lift)

Segmentation risk review (10 alignment factors)

Radar to R script mathematical verification

TECH STACK

SAS R Fraud Detection Segmentation WTW Radar

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