About Yodlee
At Yodlee Inc, we’re at the forefront of innovation, utilizing cutting-edge Machine Learning algorithms and Big Data Engineering frameworks. Our high-caliber, mission-driven culture empowers teams to tackle challenging and rewarding projects that drive impactful business decisions across the global financial services landscape.
About the role
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The Credit & Fraud Analytics Director is responsible for leading the development and management of risk analytics products and services, with a special focus on data-driven financial decisioning in credit and fraud risk management. This role involves data analysis, strategic planning, product innovation, market analysis and hands-on product development to enhance the organization's analytics offerings.
What you'll do
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Lead credit and fraud risk analytics with deep expertise in financial‑services data science.
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Shape strategy, roadmaps, and product vision for data‑driven credit and risk solutions.
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Champion products to influence leadership alignment and prioritization.
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Demonstrate new capabilities through pilots, POCs, and ad‑hoc implementations.
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Define product design elements including features, pricing, and partnership models.
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Collaborate with technology teams to integrate off‑the‑shelf and custom solutions.
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Establish processes, ensure quality compliance, and guide teams in structured problem‑solving.
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Provide technical and functional leadership to solve complex business and engineering challenges.
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Support hiring, coaching, and performance feedback while ensuring adherence to legal and compliance requirements.
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Partner with go‑to‑market teams and internal SMEs to maximize product value and client impact.
Qualifications
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5+ years of experience mentoring analytics team members and in analytics application/solution development. This refers to the implementation of repeatable analytic solutions which encompass all phases of the software development cycle, beyond model development.
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Strong experience developing analytical products or decision-support solutions within credit, fraud, or financial services domains, with demonstrated understanding of regulatory considerations and risk management frameworks.
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Hands-on data analytics experience, including exploratory data analysis, feature engineering, and translating large, complex datasets into deployable solutions that drive measurable business outcomes.
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8+ years of professional experience and a Master of Arts/ Science or equivalent degree in computer science or related area of study
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4+ years use of R, SQL for data mining and analysis.
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Strong programming skills in Python and familiarity with statistical and ML frameworks
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Proven ability to collaborate across diverse functional areas, bridging technical and business stakeholders. Must excel at connecting business requirements to data mining objectives and to measurable business benefit.
