Historical Market Data (HMD)

Why Mizuho
At Mizuho, we provide the stability of an international industry leader with the career trajectory of a growing business. Our steady, strategic growth gives our people at all levels rewarding degrees of responsibility and richer work experience than a boutique firm or an established giant could offer alone
It’s the local expertise of our employees that makes our global network so powerful. By collaborating with colleagues and clients who have the same ambition and drive, you can amplify your sphere of influence and base of knowledge as part of one of the largest and growing banks in the world.
Role Overview:
The Historical Market Data (HMD) team is a specialist function within Enterprise Risk responsible for the quality, completeness, and controlled implementation of market risk time series used in Value at Risk (VaR), Stressed VaR (SVaR), stress testing, and related risk and capital calculations.
Reporting to the Head of Risk Appetite and Capital Analysis, the candidate will own front-to-back onboarding and maintenance of historical risk-factor data across spot, curves, spreads, and volatility surfaces. The role requires strong understanding of market risk time-series construction, returns and shock generation, proxy and backfill methodologies, data-quality controls, and the downstream impact of data changes on risk measures. The successful candidate will work closely with Market Risk, Risk Analytics, Quantitative Analytics, and Technology teams.
Key Responsibilities:
Lead front-to-back onboarding of historical market data and risk-factor time series, including spot, curve, spread, and volatility surface data.
Manage time-series implementations end to end, covering requirements analysis, data sourcing, mapping, transformation, validation, testing, and production delivery.
Assess the suitability and completeness of market data used in VaR, SVaR, stress testing, FRTB, and related capital calculations.
Perform data-quality analysis across completeness, accuracy, consistency, timeliness, outliers, stale observations, and risk-factor coverage.
Develop, document, and enhance proxy and backfilling methodologies for illiquid, sparse, or unavailable historical time series.
Conduct quantitative impact assessments for data-source changes, history extensions, methodology updates, and proxy enhancements.
Investigate material data exceptions and production issues, identify root causes, and drive remediation with Risk Analytics and Technology teams.
Partner with Quantitative Analytics and Market Risk stakeholders on risk-factor modelling, methodology changes, and model-data requirements.
Define test scenarios, execute UAT and regression testing, validate results, and coordinate issue resolution for HMD platform changes.
Contribute to automation, monitoring, and tooling enhancements that improve the scalability, transparency, and control of time-series management.
Prepare management information on data quality, proxy usage, backfilling activity, risk-factor coverage, exceptions, and remediation progress.
Maintain clear documentation of data lineage, source selection, transformations, proxy logic, controls, approvals, and implementation outcomes.
Support governance, audit, model-risk, and regulatory reviews relating to historical market data and market risk measurement.
Qualifications and Experience:
Master's degree from a Tier 1 or Tier 2 university in Financial Engineering, Mathematics, Statistics, Quantitative Finance, Economics, Data Science, Engineering, or a related quantitative discipline.
5-12 years of relevant experience within Market Risk Time series, Quantitative Risk, Risk Analytics, Market Data, Product Control, or a closely related function in a bank or financial institution.
Hands-on experience with historical time-series data, market-data validation, proxy construction, backfilling, risk-factor mapping, or data-quality controls.
Strong analytical and problem-solving skills, with the ability to interpret complex data flows and communicate quantitative issues clearly.
Effective stakeholder-management skills and the ability to work across Risk, Quantitative Analytics, Technology, and control functions.
Strong Python and SQL skills for data analysis, validation, automation, and investigation; advanced Excel proficiency required.
Experience working with large-scale historical time-series datasets and applying statistical techniques to identify gaps, outliers, and structural data issues.
Familiarity with market-data vendors, risk engines, or platforms such as Bloomberg, LSEG/Refinitiv, Murex, MSCI RiskMetrics, or equivalent systems is advantageous.
CFA, FRM, CQF, or an equivalent professional qualification is advantageous.
Organization Overview:
Mizuho Global Services (MGS), Pune is an integral part of Mizuho Financial Group, one of the world’s leading financial institutions with a strong global presence across the Americas, EMEA, and Asia. Based in India, MGS Pune supports Mizuho’s international businesses by delivering high-quality, scalable, and resilient services across multiple functions.
MGS Pune plays a critical role in driving operational excellence, standardization, and innovation for Mizuho Americas. By combining deep domain expertise with strong process, technology, and analytical capabilities, it partners closely with regional and global teams to support corporate and investment banking, capital markets, and corporate services functions, while adhering to the highest standards of risk management, regulatory compliance, and control.
MGS Pune offers competitive compensation and benefits package aligned with industry standards and local market practices. MGS Pune is an equal opportunity employer and is committed to fostering an inclusive and diverse workplace. Employment is subject to applicable background verification checks in accordance with Indian laws and company policies.
https://www.mizuhogroup.com/asia-pacific/mizuho-global-services/careers

Work arrangement
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