Head of Data Science

Collinson

Collinson is the global, privately-owned company dedicated to helping the world to travel with ease and confidence.

We use our expertise and products to craft customer experiences which enable some of the world’s best known brands to acquire, engage and retain the most demanding and choice-rich customers. In particular, our unique expertise and insight into high earning, frequent travellers allows us to create products and solutions for our clients that inspire greater customer engagement to drive more profitable relationships, enrich their travel experiences, protect what matters and assist in in times of need.

While specialising in Financial Services, Travel and Retail, we also support clients in multiple sectors. We have worked with over 90 airlines, 20 hotel groups and more than 600 financial institutions and banks, with clients including Accor Hotels, Air France KLM, American Express, British Airways, Cathay Pacific, Diners Club, Mandarin Oriental, Mastercard, Radisson Hotel Group, Sephora, Visa and Vhi.

We take our 30 years’ experience working with these kinds of household names in over 170 countries, and help our clients to deliver the smarter experiences it takes to differentiate their propositions, and help them win deeper devotion with their customers.

Collinson is a privately-owned entrepreneurial business with 2,000 passionate people working in 20 locations worldwide. Our solutions include Priority Pass, the world’s best known airport experiences programme, while we are also the trusted partner behind many of the leading financial services, airline and hotel brand’s reward programmes and loyalty initiatives.

Purpose of the job

The Head of Data Science is a senior position responsible for shaping, operationalizing, and advancing the organization’s data science strategy. This role focuses on creating high-value, data-driven insights and solutions that drive innovation, improve decision-making, and deliver measurable business outcomes. You will act as the bridge between business strategy and advanced analytics execution, ensuring that data science becomes a central enabler of enterprise growth, customer engagement, and operational excellence.

By leading a portfolio of data science initiatives, you will collaborate with senior stakeholders across business units, translating high-level business objectives into scalable, production-grade data science solutions. This role demands deep technical expertise, strong leadership skills, and the ability to deliver solutions that strengthen the organization’s competitive advantage.

The ideal candidate is a visionary leader who understands both the technical and business aspects of data science. You will partner with teams across the company to define strategies, deliver impactful projects, and embed data science into core business operations.

Key Responsibilities

•Establish and lead a high-performing Data Science function, fostering an environment of innovation, experimentation, and analytical excellence.

•Own the organizational data science strategy, including defining and delivering the intelligent data products vision and roadmap.

•Drive the full lifecycle of data science projects, from problem framing and data exploration to model development, validation, deployment, and scaling.

•Partner with senior leaders across platform, product, and business units to refine and execute the organizational data science strategy, ensuring alignment with business goals.

•Collaborate with stakeholders to identify high-impact use cases and prioritize initiatives that deliver measurable business value.

•Deliver advanced analytics and machine learning solutions addressing challenges such as customer personalisation, operational optimisation, fraud detection, predictive forecasting and more.

•Manage and mentor a team of data scientists, developing their technical and business skills while aligning their efforts with organizational priorities.

•Oversee the integration of data science solutions into existing systems, ensuring robustness, scalability, and long-term performance.

•Promote innovation by exploring new data science applications beyond current ecosystem limitations.

•Champion best practices in data governance, ethical data standards, and regulatory compliance.

•Stay ahead of emerging trends in data science, analytics, and AI to introduce innovations that reinforce the organization’s market leadership.

•Lead the adoption of robust experimentation and causal inference methods (A/B testing, uplift testing) to embed an evidence-based culture across the business.

•Partner closely with Data Engineering and Platform teams to ensure high-quality pipelines, reliable feature stores, and scalable production deployments.

•Drive the operationalization of predictive models in areas such as demand forecasting, customer lifetime value, churn prediction, and fraud detection.

•Champion the use of advanced analytics for decision support, ensuring insights are actionable, timely, and embedded into business processes.

•Ensure all initiatives deliver measurable commercial ROI, linking data science outcomes to customer loyalty, revenue growth, and cost optimization.

Impact Areas

•Enable data-driven products and solutions that enhance customer acquisition, loyalty, and retention.

•Operational Efficiency: Drive cost optimization and decision-making through advanced analytics and predictive modelling.

•Customer Experience: Deliver personalized, real-time services powered by data insights and advanced analytics.

•Experimentation Culture: Build a company-wide discipline of testing, learning, and scaling insights.

•Commercial Value Realization: Ensure every data science initiative is measured against its impact on revenue, retention, and efficiency.

•Scalable Deployment: Deliver models that are production-ready, resilient, and aligned with enterprise-grade standards

Knowledge, skills, and experience required

•Proven track record in leading data science projects with measurable business outcomes in a commercial setting.

•Expertise in statistical modelling, machine learning, predictive analytics, and data-driven decision-making.

•Strong experience with data science tools, frameworks, and cloud platforms.

•Proficiency in designing and managing end-to-end data science workflows, including data preparation, model training, deployment, and monitoring.

•Leadership experience managing teams of data scientists or analysts in fast-paced, innovation-driven environments.

•Ability to engage with C-suite stakeholders, translate business needs into analytical solutions, and communicate results effectively.

•Deep understanding of data ethics, privacy, and governance frameworks.

•Proven expertise in applied machine learning, predictive modelling, and statistical analysis within large, complex organizations.

•Strong track record of deploying and maintaining models in production, with emphasis on scalability, monitoring, and lifecycle management.

•Experience designing and leading experimentation frameworks to validate business impact.

•Proficiency in working with modern data architectures (e.g., lakehouse, feature stores, streaming systems) to enable real-time analytics and ML.

•Commercial acumen with the ability to translate analytical outputs into tangible business outcomes.

Person Specification

•Self-motivator with a desire to learn new skills and embrace new technologies in a constantly changing technology landscape

•Ability to thrive in a fast moving environment

•Ability to show initiative, innovation and work independently when required

•Ability to work at pace and tackle project challenges in a collegiate, collaborative way

•Goal and outcome orientated

•Thoroughness and attention to detail

•Good communication skills (ability to present, inform and guide others)

•Ability to bridge communications between technical and business focussed groups

•Comfortable working with people at all levels in an organisation