Director of Data Science - Credit Risk

Harnham; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Harnham partners with a high-growth consumer fintech to lead acquisition modelling strategy at scale, owning predictive models and balancing growth, risk, and regulatory considerations. This is a rare opportunity for an experienced Data Science leader to shape strategy, influence executives, and build the function during a significant growth phase.

You will lead a team and set governance across the modelling lifecycle.

Qualifications

• Significant experience leading Data Science teams and delivering production-grade predictive models.
• Background in credit risk or regulated environments.
• Strong understanding of model governance, monitoring and explainability.

Responsibilities

• Own acquisition modelling strategy and drive scalable ML models.
• Lead and develop a Data Science team, partnering with stakeholders.
• Balance business growth with risk, regulatory and customer outcomes.
• Set governance standards across the modelling lifecycle.

Skills

Data science leadership

Predictive modelling

Model governance

Production ML deployment

Credit risk understanding

Job description

We're partnering with a high-growth consumer fintech that is advancing how it's credit decisions are made, leveraging machine learning to balance risk, growth and profitability objectives.

This is a rare opportunity for an experienced Data Science leader to own acquisition modelling strategy at scale while remaining close to technical decision-making. You’ll lead a team responsible for some of the organisation's most important predictive models, balancing growth, risk, customer outcomes and regulatory considerations.

The business has moved beyond early-stage uncertainty but still offers the opportunity to shape strategy, influence executive stakeholders and build a function during a significant growth phase.

Why this role is interesting

How do you identify creditworthy consumers where conventional signals are incomplete, inconsistent or insufficient? How do you expand approvals without compromising portfolio performance? How do you build models that remain explainable, compliant and resilient as economic conditions evolve?

These questions sit at the centre of the role.

The business has reached an interesting stage of maturity. The foundations have been built, the models are operating at scale, and leadership now wants to continue pushing the sophistication of its credit decisioning capabilities.

You’ll have significant influence over what that next chapter looks like.

The technical challenge

Unlike many machine learning applications, success isn't immediately visible.

The models you build today may take months or even years before their true predictive power is fully realised. That requires strong judgement around experimentation, monitoring, validation and model risk management.

You'll be operating in a highly regulated environment where model performance matters, but so do fairness, transparency and explainability. The ideal candidate understands how to balance those competing priorities without losing sight of the business objective.

Technology Environment

The team works within a modern machine learning ecosystem including:

• Python
• NumPy
• Scikit-Learn
• LightGBM
• Airflow

This isn't a hands-on IC role, but strong technical credibility is essential. You'll be setting standards and challenging decisions made across the modelling lifecycle.

You’ll likely enjoy this role if...

• You're energised by business problems rather than modelling techniques alone
• You enjoy operating as a strategic leader while remaining technically credible
• You've worked on long-horizon prediction problems where outcomes take months or years to develop
• You believe strong machine learning requires equal attention to governance, monitoring and business context
• You enjoy helping experienced Data Scientists raise their game rather than being the smartest person in the room
• You want visibility with executive leadership and influence over company-wide decision making

What they're looking for

• Significant experience developing and deploying predictive models in production
• Experience leading Data Science teams
• Background in credit risk, underwriting, lending, insurance or another regulated predictive environment
• Strong understanding of model governance and monitoring
• Ability to translate commercial objectives into modelling strategy