Head Of Data Science & Credit Risk
New
F
FINNFintech
GlobalFull-TimeManager
SalaryA competitive salary based on experience and location.
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Job Details
- Experience
- At least 10 years
- Required Skills
- SQLMachine LearningData scienceA/B testingMLOps
Requirements
- At least 10 years of combined experience in data science, machine learning, and consumer credit risk within fintech, digital lending, BNPL, or earned wage access.
- Experience developing and managing credit policies and portfolios at scale across multiple products or markets.
- Proven track record of building, deploying, and monitoring production ML models within real-time or near-real-time decisioning pipelines.
- Hands-on experience with experimentation and A/B testing to evaluate model and policy changes.
- Strong statistical and mathematical skills, with expertise in classical statistical methods and modern machine learning.
- Working knowledge of SQL and exploratory data analysis, alongside experience with cloud data platforms.
- Experience building and leading technical teams while remaining actively involved in model development and problem-solving.
- Strong communication skills with the ability to explain complex models and credit risk concepts to business stakeholders.
- Sound business judgment and the ability to connect technical decisions to growth, portfolio performance, and ROI.
- Adaptable, proactive approach to working in a fast-paced startup environment.
Responsibilities
- Lead the design, testing, and deployment of ML models for credit decisioning, fraud detection, and risk segmentation.
- Develop underwriting algorithms using alternative data sources to expand financial access.
- Develop and manage credit risk frameworks, policies, and approval strategies adapted to each market.
- Set risk thresholds and customer segmentation strategies balancing growth, default rates, and portfolio health.
- Build, lead, and mentor a team of data scientists and risk analysts while remaining hands-on with technical work.
- Communicate model performance, portfolio trends, and strategic recommendations to the executive team and board.
- Partner with engineering, product, and finance to translate analytical insights into business outcomes.
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