Hi, I'm Nyra!

Jack of all trades. I intend to master them all. I’ve always been curious about how things work, whether that means learning martial arts, exploring the culinary arts, solving a mathematical problem, or building something with code. At the University of Waterloo, I’m studying Computer Science and Finance, where I get to combine two very different ways of thinking: building systems and understanding how they create value. My interests span AI, machine learning, data, quantitative finance, and whatever I happen to be curious about next.

Experience Projects

Education

2023 - 2028

University of Waterloo, Ontario, Canada

Computer Science and Finance (Double Major)

Minors: Statistics and Computational Mathematics

Honours & Co-operative Program

Data Structures & Algorithms Object-Oriented Programming Logic & Computation Machine Learning in Quantitative Finance Probability & Statistics Statistical Modeling Regression & Forecasting Linear Algebra Calculus Financial Data Analytics Fixed Income Securities
2011-2023

St. Joseph's School


• School Top Scorer Grade 12 (98.4%) and Grade 10 (97.6%)


• Subject Top Scorer - Mathematics (99%), Computer Science (100%), English (99%), Chemistry (99%)

Experience

Sept 2026 – Present

Data Science Intern (Software Platform)

Harbinger Motors

• Developing Python backend services and data pipelines to decode vehicle telemetry, validate structured data, and process time-series data using FastAPI, Pydantic, and PostgreSQL.


• Building predictive modeling workflows by integrating time-series forecasting and anomaly detection components to generate vehicle performance forecasts and identify unusual telemetry patterns.

Jan 2026 – Apr 2026

Data Science and ML Intern

Rocket Companies

• Developed machine learning pipelines to score 100,000+ mortgage leads using Python, SQL, XGBoost, and AWS.


• Engineered behavioral and rate-sensitive features and evaluated model calibration and performance across customer segments.

May 2025 – Aug 2025

Data & Automation Developer

Financial Services Regulatory Authority of Ontario (FSRA), Toronto, Ontario

• Built Python and SQL tools to automate pension plan screening and support risk-based analysis across 3,900+ pension plans.


• Developed workflow automation and an AI-powered chatbot to streamline internal processes, reducing screening turnaround time by 98% and inquiries by 35%.

Sept 2025 – Dec 2025

Research - LLM-Based Data Compression with RAG

University of Waterloo, Waterloo, Ontario

• Conducted research on lossless data compression using large language models, applying autoregressive token prediction and RAG to improve efficiency while minimizing storage requirements.

May 2025 – Aug 2025

Coding Instructor

CodingPlusOne

• Designed and delivered comprehensive programming curriculum for students aged 8-16, teaching Python fundamentals and Scratch visual programming to develop computational thinking and logical reasoning skills.


May 2024 – Aug 2024

AI Solutions Developer

FinTech Lab, Kitchener, Ontario

• Evaluated and benchmarked 9+ APIs, improving overall system performance, ensuring interoperability across platforms, and strengthening data engineering and analytical expertise.


• Integrated, transformed, and analyzed datasets from Google Analytics APIs, generating actionable insights that improved reporting accuracy and directly supported data-driven decision-making.

Jun 2024 – Jul 2024

AI Research and Prototyping Engineer

InvestAlice, Toronto, Ontario

• Designed and developed an expense analyzer prototype using JavaScript, React, and Tailwind CSS, enhancing data visualization and user interfaces.


• Conducted research to identify suitable AI technologies for financial services and analyzed competitors' AI implementations to benchmark and innovate solutions.

Skills and Certifications

Programming Languages:

Python, C++, C, Java, SQL (MySQL, PostgreSQL, Microsoft SQL Server), R, JavaScript, Bash, Shell Scripting

Machine Learning & Data Science:

scikit-learn, PyTorch, TensorFlow, XGBoost, statsmodels, Pandas, NumPy, Matplotlib, Seaborn, NLTK, Streamlit, yfinance, Hugging Face

Backend & Software Development:

FastAPI, Flask, Node.js, React, Next.js, REST APIs, Pydantic, Celery, Redis

Cloud, Data & Developer Tools:

AWS (SageMaker, S3, Athena, Glue), Microsoft Azure, Git, Linux, Docker, Jupyter Notebook

Analytics & Productivity:

Power BI, Power Automate, Microsoft Copilot Studio, Excel, Figma

Certifications:

• Microsoft Certified: Azure AI Fundamentals (AI-900)
• Microsoft Certified: Azure Fundamentals (AZ-900)
• Undergraduate Research Training Course - Computing Research Association (CRA)

Projects

Biquadris Project

Biquadris

Game Logic Design and Development

  • Developed a turn-based, two-player C++ game inspired by Tetris, implementing 5+ scalable difficulty levels and 3+ special actions, with both text-based and graphical interfaces using X11.
  • Applied object-oriented programming principles, designing 10+ modular components for gameplay mechanics, block generation, and board management, ensuring efficient and maintainable code.
  • Enhanced user experience by implementing a robust command interpreter, refining 50+ player interactions, and conducting 100+ test cases to ensure seamless and reliable gameplay.
Robo-Advising Project

Robo-Advising

Portfolio Optimization Strategy Development

  • Developed a Python-based stock portfolio optimization algorithm, achieving a 12% improvement in risk-adjusted returns compared to market benchmarks, leveraging Beta, Sharpe Ratio, Moving Average, and P/E Ratio.
  • Dynamically filtered and analyzed 500+ stocks from a dataset, optimizing risk-return balance through comprehensive data-driven evaluation.
  • Visualized historical stock performance to validate portfolio strategy and created detailed final outputs, including weighted allocation and projected outcomes, showcasing advanced quantitative and programming skills with the use of NumPy and Pandas for data processing and statistical calculations.
Federato Project

Federato RiskOps Platform

Platform Optimization Challenge

  • Conducted Exploratory Data Analysis (EDA) on 10K+ user interactions using Python and R, uncovering regional, role-based, and behavioral engagement trends to optimize user journeys and platform retention.
  • Built and fine-tuned a predictive model with 90%+ accuracy using Random Forest classifiers to forecast user actions based on session behavior.
  • Identified key drop-off patterns, showing that 44% of guest users disengaged early, and proposed actionable strategies to improve onboarding and session retention.
NutriSync Project

NutriSync

Personalized Meal Planning System Development

  • Developed a personalized meal planning application using Python and MySQL, generating customized diet plans for 10+ unique dietary profiles based on medical conditions and preferences.
  • Engineered a dynamic recipe and ingredient management system, automating meal suggestions with 90%+ accuracy and generating grocery lists, reducing manual meal planning time by 50%.
Toronto Transportation Project

Toronto Transportation Network

Traffic Optimization and Analysis

  • Analyzed Traffic Data: Collected and processed real-time traffic data from multiple intersections in Toronto, focusing on vehicle, pedestrian, and bike traffic volumes.
  • Identified Congestion Patterns: Used data analysis to identify traffic slowdowns, peak congestion times, and key locations with the highest traffic bottlenecks, including lane-specific traffic behaviors.
  • Optimized Traffic Flow: Developed data-driven solutions to optimize bus routes, reduce congestion, and improve overall urban mobility by analyzing key variables affecting traffic efficiency.
Data-Driven Tail Risk Project

Data-Driven Tail Risk: VaR/ES Estimation & Forecasting

Risk Analysis with Public Market Data

  • Applied statistical and machine learning techniques to estimate and forecast Value at Risk (VaR) and Expected Shortfall (ES) using historical, Gaussian, and Student-t methods across multiple asset classes.
  • Implemented quantile regression models and backtesting frameworks in Python to evaluate risk model calibration, enhancing understanding of tail-risk behavior in financial markets.
  • Developed comprehensive risk assessment tools analyzing portfolio exposure and market volatility patterns across different economic conditions.
Semantic Book Recommender Project

Semantic Book Recommender System

NLP-Based Recommendation Engine

  • Developed a personalized book recommendation engine leveraging NLP and semantic analysis to understand contextual meaning of book descriptions and metadata.
  • Processed and analyzed 7K+ book entries, extracting semantic features from textual and categorical data to capture nuanced themes and writing styles.
  • Built a machine learning pipeline using Python (pandas, scikit-learn, NLTK) to generate context-aware recommendations, improving relevance compared to keyword-based systems.
Housing Price Prediction Project

Housing Price Prediction

Real Estate Market Analysis

  • Developed a machine learning model to predict real estate prices based on key property and location features, conducting comprehensive EDA to uncover relationships between square footage, rooms, amenities, and proximity to city center.
  • Implemented data preprocessing, feature scaling, and selection techniques to improve model accuracy and interpretability.
  • Trained and evaluated Linear Regression models, analyzing feature importance and visualizing correlations between property characteristics and sale prices using Python (pandas, scikit-learn, matplotlib).
LegacyTree Project

LegacyTree: AI-Powered Family Storytelling Platform

AI-Driven Storytelling Platform

  • Built LegacyTree, an AI-driven platform that helps users record, summarize, and visualize family stories through interactive maps and illustrated chapters.
  • Enabled story recording via audio, text, or image uploads with integrated speech-to-text and text-to-speech in multiple languages.
  • Implemented AI summarization and theme extraction using HuggingFace Transformers, integrated Stable Diffusion for AI-generated illustrations, and designed a full-stack system using Streamlit and FastAPI.
Mean-Variance Optimization Project

Mean-Variance Optimization: Modern Portfolio Theory in Action

Financial Portfolio Analysis

  • Conducted extensive financial analysis applying Harry Markowitz's Modern Portfolio Theory (MPT) to historical market data (2000–2023), analyzing 27 selected stocks and the S&P 500 index.
  • Performed comprehensive risk-return analysis computing key metrics including mean returns, volatility, skewness, kurtosis, and 5% Value at Risk (VaR).
  • Built and visualized the Efficient Frontier to identify optimal portfolios, demonstrating diversification benefits and providing actionable insights on risk management and asset allocation.

Recommendations

"I was consistently impressed by her resourcefulness and dedication. Nyra consistently delivered to the expectations and took ownership of her projects, ensuring their successful completion.

I highly recommend Nyra for any role as she is multi-talented. She is a motivated and adaptable individual who will undoubtedly make significant contributions to any organization."


Allan Abbas

Founder of FinTech Lab

"Beyond academics, Nyra proved herself to be an outstanding all-rounder. She took on leadership roles with zeal, serving as a school parliament member and literary secretary.

Her ability to balance her academic responsibilities with her extracurricular commitments is a testament to her exceptional time management skills and leadership qualities."


Julie Kurian

Computer Science Teacher

"Nyra demonstrated exceptional technical expertise, analytical acumen, and a collaborative spirit that significantly contributed to our team's success. Beyond her technical and interpersonal skills, Nyra displayed a strong commitment to continuous learning and professional growth.

Her initiative in seeking feedback and refining her skills underscored her dedication to excellence."


Victor Wong

Director of PBGF and Strategic Transformation