Toronto, Canada | UTSC Statistics Co-op

MD Awsaf Hossain

Aspiring Quant | CS + Statistics + Math

I build quantitative finance and data projects in Python and C, spanning options pricing, volatility modelling, backtesting, and analytics.

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About Me

I'm a final-year Statistics student in the Quantitative Finance stream at the University of Toronto Scarborough, in the co-op program. My training combines business and quantitative methods: I program in Python and C, and my current coursework covers stochastic processes, numerical methods, and optimization. I've applied this in finance roles at Heineken Canada, where I spent eight months with the finance and business analyst team, and at Teesta Solar. Outside of class, I build projects in [e.g., strategy backtesting / derivatives pricing / market data analysis] to turn theory into working models. I'm working toward a career in quantitative finance.

Education

University of Toronto Scarborough

Honours Bachelor of Science, Statistics Co-op – Quantitative Finance Stream

Relevant coursework: Probability, Statistics, Stochastic Processes, Linear Programming and Optimization, Numerical Algorithms for Computational Mathematics, Introduction to Derivative Markets, Calculus I/II, Multivariable Calculus, Linear Algebra I/II, Differential Equations, Discrete Mathematics, Computer Science I/II, Investments and Credit.

Skills

Quant Research

Backtesting, market data analysis, strategy evaluation, regime testing

Derivatives & Pricing

Black-Scholes pricing, implied volatility surfaces, options and futures, OptionMetrics (WRDS)

Mathematics

Multivariable Calculus, Linear Algebra, Differential Equations, Linear Programming & Optimization, Numerical Methods

Probability & Statistics

Probability Theory, Stochastic Processes (in progress), Statistical Inference, R

Programming

Python (NumPy, pandas), C, R, SQL, VBA, Git

Financial Analysis & Tools

DCF valuation, financial modeling, forecasting, sensitivity analysis, Excel, Power BI, Alteryx

Experience

Finance Intern – Heineken Canada (04/2025 to 12/2025)

Built Power BI dashboards across sales and finance datasets; supported time-series trend analysis, forecasting, pricing and discount analysis, and Python-based automation for recurring reporting workflows.

Junior Financial Analyst – Teesta Solar Ltd (06/2024 to 10/2024)

Developed a 20-year revenue model and DCF valuation model with sensitivity analysis for solar project finance decisions; helped define KPI dashboards for performance tracking.

Finance & Marketing Director – Data Science and Statistics Society, UTSC (04/2025 to 04/2026)

Managed Datathon budgeting, sponsor coordination, financial documentation, and KPI tracking to support financially sustainable and well-executed student events.

Co-Founder – ChattoTurf Sports Arena (02/2021 to Present)

Co-founded Chattogram's first commercial sports arena and scaled it from one location to four. Lead a team of 17 across operations, marketing, and sales, and own financial planning and cash-flow modeling to guide expansion decisions.

Featured Projects

Implied Volatility Surfaces and the Variance Risk Premium

Building implied volatility surfaces from OptionMetrics index option data and comparing the variance risk premium across US and Canadian equity exposure. Black-Scholes priced on the forward, implied volatility recovered by Brent inversion, SVI surface fits tested for butterfly and calendar arbitrage violations.

Status: In progress. Pricer and analytic vega complete, validated against put-call parity, monotonicity in volatility, payoff convergence at expiry, and a finite-difference check.

Design note: UofT's WRDS subscription has no Canadian IvyDB coverage, so the Canadian leg uses NYSE-listed EWC options. This measures how US investors price Canadian equity risk, currency-unhedged, which is documented as a limitation.

PythonSciPySQLWRDS / OptionMetricsOptions Pricing
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Quant Trading Strategy Backtest

Python backtesting framework using TSLA market data with transaction costs, signal rules, volatility targeting, and market regime testing.

Result: Strategy edge weakened out-of-sample after realistic cost controls.

PythonBacktestingMarket Data
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Ingredient Networks Graph Project in C

Built a graph-based ingredient network using an adjacency matrix, recursive k-hop search, and exclusion filtering with scalability to 400 ingredients.

Method: Recursive graph traversal and matrix-based relationship scoring.

CGraphsRecursion
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Music Data Structures Project in C

Implemented binary search trees and linked structures with recursion, file parsing, traversal, deletion, reversal, and harmonization logic.

Thesis: Efficient structure design improves correctness and speed in low-level C workflows.

CData StructuresBST
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Movie Review Database Project in C

Designed a pointer-based linked-list database supporting insert/search/update/sort/delete operations and profitability analysis.

Method: Pointer-safe linked-list operations with modular query and ranking logic.

CLinked ListsDatabase Logic
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Ontario Bridges Data Analysis Project in Python

Processed CSV infrastructure data to analyze bridge conditions, perform distance-based filtering, and build priority ranking logic for inspection planning.

Result: Produced a reproducible prioritization workflow for inspection decision support.

PythonData AnalysisCSV Processing
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Currently Building Toward Quant Finance

  1. Advance Python workflows for cleaner research and faster analysis cycles
  2. Deepen probability, statistics, and stochastic reasoning for market uncertainty
  3. Expand backtesting research with stronger risk controls and regime awareness
  4. Use C and data structures to strengthen algorithmic and systems thinking
  5. Apply financial modeling and valuation in practical investment scenarios
  6. Next focus: options pricing, portfolio optimization, and ML for market signals

Resume & Contact