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Responsibilities
Identify, design, and implement solutions—from traditional analytical approaches to AI/ML—to tackle Treasury’s most pressing challenges.
Build, maintain, and version Python libraries; follow SDLC best practices and publish code on GitHub with robust documentation.
Manage the model pipeline and associated governance prescribed by Model Risk Management.
Extract, cleanse, and prepare large datasets, leveraging SQL and other data manipulation techniques for advanced analytics pipelines.
Develop high-impact Tableau dashboards that translate complex data insights into actionable recommendations.
Drive platform and model strategy, project prioritization, and forward-looking analytics in alignment with firmwide objectives.
Collaborate across Capital, IRRBB, FTP, Non-Market Risk, and Methodology teams to ensure timely, data-driven solutions that enhance business decision-making.
Qualifications
10+ years of relevant finance/banking experience, with proven expertise in Balance Sheet Management, Capital, IRRBB, Transfer Pricing, or similar.
Strong foundation in mathematics, quantitative finance, programming, and data science, with experience building and deploying advanced AI/ML solutions.
Proficiency in Python, SQL, and data engineering practices; familiarity with analysis tools (Tableau, Excel) and version control (GitHub).
Demonstrated ability to architect, maintain, and scale analytics pipelines—from data ingestion to model deployment—within SDLC frameworks.
Adept at capital markets activities, liquidity planning, and balance sheet analytics.
Exceptional communication, leadership, and collaboration skills can influence technical and non-technical stakeholders.
Bachelor’s degree (Master’s preferred) in a relevant field (Finance, Computer Science, Data Science, Mathematics, or similar).
Additional Job Description
Coding, Python, Tableau, understanding and able to design Quantitative Method., Data Science
Non-Trading Market Risk Management
Time Type:
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