Head of Data Science

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. Analytic 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 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