Our client is on a mission to redefine how trust is established in B2B relationships. As a fast-growing commercial risk data and analytics company, their proprietary platform gives businesses unmatched visibility into 20M U.S. businesses by blending leading public and regulatory sources with exclusive, peer-contributed data — powering smarter decisions across customer acquisition, underwriting, fraud prevention, and portfolio monitoring.

Having recently closed a significant Series A funding round, our client is entering their next phase of growth and is now looking to hire a Head of Data Science to lead the build-out of their data science function. Reporting directly to the CEO, this is a rare opportunity to join a high-growth fintech at a pivotal moment — shaping the analytical products that sit at the heart of the business and driving measurable commercial outcomes across embedded retail lending, BNPL, SMB lending, payment processing, and healthcare finance.
If you're a hands-on data science leader with deep expertise in risk modelling, alternative data, and a passion for turning complex data into scalable, revenue-generating products — this is the role for you.

Analytical Product Development
•    Own end-to-end development of analytic products—from raw data ingestion to scalable, production-ready outputs
•    Design and iterate on features, attributes, and models that convert proprietary data assets into differentiated, commercial products
•    Partner with Product and Engineering to ensure solutions are robust, scalable, and embedded into workflows
Scorecard & Attribute Development
•    Design, build, and refine risk scores and predictive attributes across multiple use cases
•    Manage and maintain multiple scorecard versions simultaneously
•    Produce clear, audit-ready documentation of model methodologies to support client compliance and transparency
Machine Learning & Data Science Execution
•    Develop and deploy machine learning models using Python, SQL, and modern ML frameworks
•    Conduct exploratory data analysis to identify trends, signals, and opportunities
•    Ensure data quality through rigorous preprocessing, validation, and monitoring
•    Collaborate with data engineering to build scalable pipelines and support production ML workflows
Alternative Data Strategy
•    Lead ingestion and productization of external and alternative data sources (e.g., cash-flow data, commerce platforms, vertical SaaS systems)
•    Translate raw external data into structured attributes and predictive signals beyond traditional bureau data
•    Identify new data partnerships that expand the organisation's data moat and product capabilities
Proof of Concept (POC) Delivery
•    Partner with go-to-market teams to design and execute analytical POCs for prospective clients
•    Translate client use cases into compelling, data-driven demonstrations that accelerate sales cycles
•    Rapidly prototype and iterate models to showcase measurable value
Team Leadership & Scaling
•    Build, mentor, and scale the data science team
•    Establish best practices for modeling, documentation, experimentation, and deployment
•    Foster a culture of high standards, ownership, and continuous learning
Client & Stakeholder Engagement
•    Engage directly with clients as needed to explain methodologies and analytical outputs
•    Support onboarding and ensure deliverables meet compliance and documentation requirements
•    Communicate complex data science concepts clearly to non-technical stakeholders
Cross-Functional Leadership
•    Partner closely with Engineering, Product, and GTM teams to align on priorities and execution
•    Ensure data science initiatives are commercially relevant and tied to business outcomes
•    Define and support data governance standards for quality, security, and compliance
•    Work with IT and Engineering to ensure scalable, efficient data infrastructure

Required Qualifications
•    Advanced degree in Computer Science, Statistics, Mathematics, or related field
•    5–7 years of experience in data science, including leadership responsibilities
•    Deep expertise in machine learning, statistical modeling, and predictive analytics
•    Strong hands-on proficiency in Python and SQL
•    Proven experience building and deploying models at scale
•    Demonstrated success developing risk scores, attributes, and scorecards (including version management)
•    Experience working with alternative/non-bureau data sources (e.g., cash-flow, merchant, or platform data)
•    Strong attention to detail, particularly in model documentation and compliance requirements
•    Excellent communication skills, with the ability to translate complex concepts into business value
Preferred Qualifications
•    Experience scaling and leading high-performing data science teams
•    Background in fintech, embedded finance, SMB lending, payments, or merchant ecosystems
•    Client-facing experience supporting POCs, sales processes, and onboarding
•    Experience integrating and structuring third-party data sources (e.g., commerce platforms, vertical SaaS tools)
•    Familiarity with big data technologies (Hadoop, Spark, NoSQL) and cloud ML platforms (AWS SageMaker, GCP, Azure ML)
What Success Looks Like
•    Launch of scalable, revenue-generating analytic products powered by proprietary and alternative data
•    Measurable lift in customer acquisition, underwriting accuracy, or portfolio performance
•    A high-performing, scalable data science team with strong standards and velocity
•    Tight alignment between data science outputs and the organization's commercial strategy