{"id":3648243,"url":"https://alion.io/job/stripe-data-scientist-fx-quantitative-research-2","title":"Data Scientist, FX Quantitative Research","company":{"id":64,"name":"Stripe","domain":"stripe.com","url":"https://alion.io/company/stripe","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":78,"open_postings":137,"ghost_share":0.036,"stale_share":0.569,"repost_share":0.058,"time_to_fill_p50_days":63,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"LLM","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"AI Agents","optional":true},{"name":"Flyte","optional":true},{"name":"Machine Learning","optional":true},{"name":"Python","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-11-02T18:49:07Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-05T18:23:17Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Who we are\nAbout Stripe\nStripe 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.\nAbout the team\nData 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.\nStripe 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.\nWhat you'll do\nYou'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.\nResponsibilities\nPartner 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\nDevelop forecasting models for FX exposures and multi-currency cash flows. Quantify forecast uncertainty so it can inform hedge sizing and timing.\nCo-design hedge optimization and decision frameworks with the FX team that weigh hedge cost, residual risk, P&L volatility, and liquidity constraints\nBuild 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\nOwn the production systems that generate decision-critical FX outputs, partnering with Engineering on reliability, SLAs, monitoring, alerting, and incident response for risk-critical workflows\nPartner 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.\nProduce clear, trusted analyses and reporting that inform Treasury and Finance leadership decisions, including leadership-level reporting such as MBRs and QBRs\nUse AI tools (code assistants, LLM-based agents) to speed up modeling, analysis, and automation, while maintaining rigor, validation, and auditability\nRaise the technical bar for FX quantitative work within Data Science: methodology standards, model validation, documentation, and mentoring other data scientists\nWho you are\nWe'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.\nMinimum requirements\nBS/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\nHands-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\nStrong 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)\nProficiency in Python and SQL. You write production-quality code, not just research scripts.\nExperience taking models or analytical systems from research into production\nBusiness acumen and the ability to turn complex analyses into clear, decision-ready insights\nAbility to clearly communicate results and drive impact with cross-functional partners, including senior non-technical stakeholders\nAbility to manage multiple projects at once, with strong attention to detail\nExperience using AI tools to speed up modeling, analysis, and coding\nPreferred qualifications\nMS or PhD in a quantitative field (e.g., Quantitative / Mathematical Finance, Financial Engineering, Statistics, Mathematics, Physics, Computer Science, Economics, Operations Research)\nApplied machine learning experience in trading or risk (e.g., forecasting, execution analytics, signal research, anomaly detection)\nExperience with corporate treasury or balance-sheet FX: multi-entity exposure management, hedge programs, and treasury concepts such as liquidity, funding, and cash positioning\nExperience building risk or decision systems with engineering standards: code review, testing, monitoring, and defined SLAs\nExperience with distributed data tools (e.g., Spark) and workflow orchestration (e.g., Airflow, Flyte)\nPrior experience at a growth-stage internet, fintech, or payments company\nExperience creating leadership-level reporting, such as QBRs and MBRs\nA builder's mindset, with a willingness to question assumptions and conventional wisdom\nInterest in how data products enable automated and agentic workflows, and an understanding that model and data quality determine the reliability of every downstream decision","description_format":"text","description_chars":5732,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-11-02T23:23:44Z"}],"visa":[{"country":"CA","licensed_sponsor":true,"evidence":"Positions approved through LMIA in 12 months: 13","filings_12m":13,"filings_prev_12m":3,"green_card_filings_12m":null,"median_offered_wage_usd":null,"route":"High Wage; 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