Who we areAbout StripeStripe 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 general economy within everyone's reach while doing the most important work of your career.About The TeamData 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 doYou'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.ResponsibilitiesPartner with the FX team to build and evolve FX risk measurement: exposure aggregation across entities and currencies, VaR / Expected Shortfall, stress testing, and scenario analysisDevelop 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 constraintsBuild ML and statistical systems for trading and risk (signal research, anomaly detection on flows, execution and cost analytics), and take them from prototype to productionOwn the production systems that generate decision-critical FX outputs, partnering with Engineering on reliability, SLAs, monitoring, alerting, and incident response for risk-critical workflowsPartner 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 QBRsUse AI tools (code assistants, LLM-based agents) to speed up modeling, analysis, and automation, while maintaining rigor, validation, and auditabilityRaise the technical bar for FX quantitative work within Data Science: methodology standards, model validation, documentation, and mentoring other data scientistsWho you areWe'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 RequirementsBS/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 scienceHands-on quantitative experience at a bank, asset manager, hedge fund, trading firm, or similar financial institution, with direct exposure to FX, rates, or market riskStrong 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 productionBusiness acumen and the ability to turn complex analyses into clear, decision-ready insightsAbility to clearly communicate results and drive impact with cross-functional partners, including senior non-technical stakeholdersAbility to manage multiple projects at once, with strong attention to detailExperience using AI tools to speed up modeling, analysis, and codingPreferred QualificationsMS 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 positioningExperience building risk or decision systems with engineering standards: code review, testing, monitoring,



and defined SLAsExperience with distributed data tools (e.g., Spark) and workflow orchestration (e.g., Airflow, Flyte)Prior experience at a growth-stage internet, fintech, or payments companyExperience creating leadership‐level reporting, such as QBRs and MBRsA builder's mindset, with a willingness to question assumptions and conventional wisdomInterest in how data products enable automated and agentic workflows, and an understanding that model and data quality determine the reliability of every downstream decisionIn-office expectationsOffice-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users.
This expectation may vary depending on role, team and location.
For example, Stripes in Stripe Delivery Center roles in Mexico City, Mexico, Bengaluru, India, and Dublin, Ireland work 100% from the office.
Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss.
This approach helps strike a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility when possible.Pay and benefitsThe annual salary range for this role in the primary location is CA$142,400 - CA$213,600.
This range may change if you are hired in another location.
For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and specific location.
Applicants interested in this role and who are not located in the primary location may request the annual salary range for their location during the interview process.Specific benefits and details about what compensation is included in the salary range listed above will vary depending on the applicant's location and can be discussed in more detail during the interview process.
Benefits /...We look forward to hearing from you.At Stripe, we're looking for people with passion, grit, and integrity.
You're encouraged to apply even if your experience doesn't precisely match the job description.
Your skills and passion will stand out and set you apart especially if your career has taken some extraordinary twists and turns.
At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions.
Join us.
#J-*****-Ljbffr

📌 Data Scientist, Fx Quantitative Research (Xico)
🏢 Stripe
📍 Xico