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Added 13 mins ago
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Financial Planner
TD
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- Full-time
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Added 13 mins ago
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Added 13 mins ago
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Added 13 mins ago
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Added 28 mins ago
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Private Equity Manager
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Added 43 mins ago
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Added 44 mins ago
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Kinectrics is a wholly owned subsidiary of BWX Technologies focused on providing life cycle management services in the energy and utility sector. We develop innovative solutions for the…
Added 44 mins ago
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Materials Operations Assistant Manager
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- Full-time
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Added 58 mins ago
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Senior Manager, Clinical Quality Assurance
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Toronto, ON
- $134,000 to $184,000 CAD / year
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Added 1 hour ago
Data Scientist, FX Quantitative Research
Stripe
Toronto, ON
…together. The Treasury Finance Data Science team builds the models, risk frameworks, and decision tools behind how Stripe manages money at global scale: liquidity, funding, counterparty…
Added 1 hour ago
T
Business Systems Analyst III
TD
London, ON · Toronto, ON
- Full-time
Leads system requirements management for moderate to high complexity/risk initiatives in alignment with strategies

Stripe

Data Scientist, FX Quantitative Research

Toronto, ON, Canada

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Job details

Experience: BS/BA with 8+ years, MS/MA with 6+ years, or PhD with 3+ years of full-time experience in quantitative finance, risk modeling, quantitative research, or data science
Posting language: English
Posted: Nov 2, 2026

Original job description

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world's largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

About the team

Data Science at Stripe is a vibrant community where data scientists and data analysts learn and grow together. The Treasury Finance Data Science team builds the models, risk frameworks, and decision tools behind how Stripe manages money at global scale: liquidity, funding, counterparty risk, and foreign exchange.

Stripe moves funds across 135+ currencies every day. That creates FX exposure from merchant flows, multi-currency balances, and entity funding. In this role, you'll be a thought partner and builder alongside Stripe's FX team. Together you'll develop the quantitative foundation for how Stripe measures, forecasts, and hedges FX exposure.

What you'll do

You'll be the senior quantitative lead for FX within Treasury Finance Data Science. You'll co-build with Treasury, the FX hedging team, Finance, and Engineering: shaping the research agenda, turning ideas into rigorous models, and building the systems that put those models to work. Data Science owns these systems. They produce the decision-critical outputs and analyses that the FX team and Treasury leadership rely on. This is a hands-on role. You'll design the methodology, write the code, and ship it into production.

Responsibilities

Partner with the FX team to build and evolve FX risk measurement: exposure aggregation across entities and currencies, VaR / Expected Shortfall, stress testing, and scenario analysis
Develop forecasting models for FX exposures and multi-currency cash flows. Quantify forecast uncertainty so it can inform hedge sizing and timing.
Co-design hedge optimization and decision frameworks with the FX team that weigh hedge cost, residual risk, P&L volatility, and liquidity constraints
Build ML and statistical systems for trading and risk (signal research, anomaly detection on flows, execution and cost analytics), and take them from prototype to production
Own the production systems that generate decision-critical FX outputs, partnering with Engineering on reliability, SLAs, monitoring, alerting, and incident response for risk-critical workflows
Partner deeply with Treasury Finance stakeholders to turn business questions into quantitative frameworks. Anticipate needs and help shape FX risk strategy rather than reacting to requests.
Produce clear, trusted analyses and reporting that inform Treasury and Finance leadership decisions, including leadership-level reporting such as MBRs and QBRs
Use AI tools (code assistants, LLM-based agents) to speed up modeling, analysis, and automation, while maintaining rigor, validation, and auditability
Raise the technical bar for FX quantitative work within Data Science: methodology standards, model validation, documentation, and mentoring other data scientists

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

BS/BA with 8+ years, MS/MA with 6+ years, or PhD with 3+ years of full-time experience in quantitative finance, risk modeling, quantitative research, or data science
Hands-on quantitative experience at a bank, asset manager, hedge fund, trading firm, or similar financial institution, with direct exposure to FX, rates, or market risk
Strong foundation in statistics, probability, and time-series modeling, plus working knowledge of risk measures (VaR, Expected Shortfall, stress testing) and hedging instruments (forwards, swaps, options)
Proficiency in Python and SQL. You write production-quality code, not just research scripts.
Experience taking models or analytical systems from research into production
Business acumen and the ability to turn complex analyses into clear, decision-ready insights
Ability to clearly communicate results and drive impact with cross-functional partners, including senior non-technical stakeholders
Ability to manage multiple projects at once, with strong attention to detail
Experience using AI tools to speed up modeling, analysis, and coding

Preferred qualifications

MS or PhD in a quantitative field (e.g., Quantitative / Mathematical Finance, Financial Engineering, Statistics, Mathematics, Physics, Computer Science, Economics, Operations Research)
Applied machine learning experience in trading or risk (e.g., forecasting, execution analytics, signal research, anomaly detection)
Experience with corporate treasury or balance-sheet FX: multi-entity exposure management, hedge programs, and treasury concepts such as liquidity, funding, and cash positioning
Experience building risk or decision systems with engineering standards: code review, testing, monitoring, and defined SLAs
Experience with distributed data tools (e.g., Spark) and workflow orchestration (e.g., Airflow, Flyte)
Prior experience at a growth-stage internet, fintech, or payments company
Experience creating leadership-level reporting, such as QBRs and MBRs
A builder's mindset, with a willingness to question assumptions and conventional wisdom
Interest in how data products enable automated and agentic workflows, and an understanding that model and data quality determine the reliability of every downstream decision

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More about this job

Responsibilities

Lead quantitative research for FX risk, including exposure measurement, forecasting, stress testing, and hedge optimization. Build and operate production decision systems, partner with Treasury, Finance, and Engineering, and deliver trusted analyses and reporting to leadership.

Requirements

Requires a relevant degree and substantial experience in quantitative finance, risk modeling, research, or data science, including hands-on exposure to FX, rates, or market risk. Candidates need strong statistical and time-series skills, Python and SQL proficiency, experience productionizing models, and the ability to communicate insights and work across teams.

Skills

Quantitative Finance
FX Risk Modeling
Statistics
Probability
Time-Series Modeling
Value at Risk
Expected Shortfall
Stress Testing
Hedge Optimization
Forecasting
Machine Learning
Python
SQL
Production Systems
Model Validation
Stakeholder Communication

Visa sponsorship

Not detected in the job text

Keywords

Foreign Exchange
Treasury Finance
Quantitative Research
FX Exposure
Value at Risk
Expected Shortfall
Stress Testing
Scenario Analysis
Hedge Optimization
Hedging Instruments
Forwards
Swaps
Options
Time-Series Modeling
Forecasting
Machine Learning
Anomaly Detection
Execution Analytics
Signal Research
Python
SQL
Spark
Airflow
Flyte
Production Systems
Model Validation
Monitoring
Liquidity Management
Funding
Cash Positioning

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