Head of Data Science

Minimum years of Experience - 15 years

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, Amex, 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.