Head Of Data Science & Credit Risk

Other Positions

Remote

September 17, 2026

Application ends: December 16, 2026

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, markets, or both.
A 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 both classical statistical methods and modern machine learning.
Strong working knowledge of SQL and exploratory data analysis, alongside experience with cloud data platforms. We use GCP BigQuery, but experience with this specific platform is not required.
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 clearly to business stakeholders.
Sound business judgment and the ability to connect technical decisions to growth, portfolio performance, and return on investment.
An adaptable, proactive approach to working in a fast-paced startup environment.

RESPONSIBILITES

Lead the design, testing, and deployment of ML models for credit decisioning, fraud detection, and risk segmentation.
Develop underwriting algorithms that use alternative data sources to improve risk assessment and expand financial access.
Build and deploy real-time or near-real-time scoring models that scale across multiple markets.
Ensure models are interpretable, fair, and robust, with monitoring for accuracy, feature stability, and drift.
Establish MLOps practices for model versioning, experimentation, deployment, and ongoing monitoring.
Expand machine learning adoption across the business, including customer value, monetization, and marketing attribution.

Develop and manage credit risk frameworks, policies, and approval strategies adapted to each market.
Set risk thresholds and customer segmentation strategies that balance growth, default rates, and portfolio health.
Monitor key risk metrics, investigate significant changes, and establish early warning signals for portfolio deterioration.
Simulate policy and model changes, support A/B testing, and refine strategies using performance data and business KPIs.
Lead stress testing and expected credit loss modeling, partnering with Finance on provisioning and capital allocation.
Support market expansion through localized risk models and policies aligned with applicable regulatory requirements.

Build, lead, and mentor a team of data scientists and risk analysts while remaining hands-on with technical work.
Own the data science and credit risk roadmap, aligning priorities with business growth and expansion plans.
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 measurable business outcomes.
Evaluate and establish partnerships with alternative data providers and credit bureaus.
Build a culture of experimentation, accountability, and data-driven decision-making.

Improve approval rates while maintaining target default rates and responsible lending standards.
Reduce time-to-decision through automated underwriting and scoring.
Identify new customer segments and product opportunities through advanced analytics.
Improve unit economics through risk segmentation and customer value modeling.
Track the impact of model and policy changes, using feedback loops to improve performance over time.

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