Director of Decision Science

Who are Liberis

At Liberis we are on a mission to unleash the power of small businesses all over the world - delivering the financial products they need to grow through a network of global partners.

At its core Liberis is a technology-driven company bridging the gap between finance and small businesses. We use data and insights to help partners understand their customers real time needs and tech to offer tailor-made financial products. Empowering small businesses to grow and keep their independent spirit alive is central to our vision.

Since 2007 Liberis has funded over 50000 small businesses with over $3bn - but we believe there is much more to be done. Learn more about Liberis by visiting are you

You are a pragmatic leader who combines deep machine learning expertise with commercial credit instincts. You thrive in cross-functional settings enjoy building high-performing teams and take responsibility for models in production from design through validation to monitoring and governance. You are energised by building model solutions that enable partners to deliver great customer experiences while protecting the business.

This senior leadership role sits at an important inflection point for our product roadmap. You will lead the Decision Sciences function responsible for end-to-end credit model development validation and monitoring across a portfolio of B2B embedded-lending products which includes (but not limited to) flexible lines of credit BCA and its variants.

What youll be doing

• Lead on model development and delivery: Own end-to-end model lifecycle for approval scorecards propensity fraud and collections models across seller and buyer products. Design features run experiments iterate on multi-bureau data pipelines and productionize models by working closely with engineering teams.
• Set modelling strategy and standards: Define modelling standards validation playbooks documentation requirements and SLAs for model deployment and change control. Ensure compliance with regulatory and audit expectations for governance and explainability.
• Build and scale model monitoring: Implement monitoring for model drift population stability performance degradation and business impact. Create alerting and remediation workflows and own model refresh cadence.
• Drive technical excellence: Champion advanced ML approaches which include (but not limited to) Gradient boosted decision trees Survival or Hazard models and Bayesian models. Develop feature engineering and robust statistical techniques for driving SMB lending products. Balance complexity with interpretability and latency constraints.
• Partner with product data and commercial teams: Translate model outputs into decisioning rules pricing signals and partner-level policies. Collaborate on experimentation A/B testing and propensity-to-convert vs risk trade-offs.
• Lead Coach and Empower the team: Manage a team of 7 data scientists and analysts including the Head of Decision Analytics. Recruit mentor and create clear career trajectories; run technical reviews code and model clinics and establish a continuous learning culture.
• Communicate to stakeholders: Explain modelling choices promote growth while managing risk trade-offs and performance to executives partners and auditors through crisp written reports and presentations.

What we think youll need

• Experience: Proven building credit or risk models in financial services with substantial recent experience in B2B unsecured lending for sellers/platforms or embedded-finance ecosystems.
• Technical depth: Proven hands-on experience developing production ML models using XGBoost GBM and related techniques. Strong Python and SQL skills for feature engineering model training and data validation. Experience with model deployment frameworks and MLOps practices.
• Full model lifecycle expertise: Demonstrable experience in model design feature engineering OOT/OOS testing validation calibration and governance. Familiarity with model explainability regulatory expectations and documentation for audit.
• Product and portfolio thinking: Comfort with approval-rate vs loss-rate trade-offs renewals economics pricing impacts and partner-level P&L. Experience translating model outputs into rules and pricing strategies.
• Analytics and tooling: Strong data visualisation and analytics skills (Power BI Tableau or equivalent). Able to prototype quickly and collaborate with engineering to productionize models.
• Judgment and communication: Track record making high-impact decisions on large exposures with clear rationales and reproducible audit trails. Excellent written and verbal communication with senior stakeholders.
• Leadership: Prior people management or function-lead experience with evidence of building standards processes and a culture of continuous improvement.

Nice to have

• Prior experience with e-commerce or ISV partnership models in the US and/or UK
• Hands on experience with decision science libraries such as scikit-learn XGBoost/LightGBM/CatBoost statsmodels; familiarity with SHAP/LIME for explainability.
• Exposure to commercial bureaus and third-party data vendors (Experian Equifax D&B) and alternative data sources common in seller ecosystems.
• Working knowledge of Monitoring & quality tools such as Evidently AI WhyLabs or equivalent for drift/PSI; Great Expectations for data tests.
• General familiarity with ML platforms & MLOps: AWS SageMaker (Studio Pipelines) Databricks ML MLflow Feature Store Docker/Kubernetes for serving CI/CD (GitHub/GitLab) orchestration (Airflow/Prefect) and IaC where relevant.
• Working knowledge of implementing Agentic AI solutions at scale

What happens next

Think this sounds like the right next move for you Or if youre not completely confident that you fit our exact criteria apply anyway and we can arrange a call to see if the role is fit for you. Humility is a wonderful thing and we are interested in hearing about what you can add to Liberis!

Our hybrid approach

Working together in person helps us move faster collaborate better and build a great Liberis culture. Our hybrid working policy requires team members to be in the office at least 3 days a week but ideally 4 days. At Liberis we embrace flexibility as a core part of our culture while also valuing the importance of the time our teams spend together in the office.

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