Senior Data Science Manager - Marketing

THE ROLE We’re looking for a Senior Data Science Manager to lead the team building the models that power our global marketing engine. You’ll be the strategic partner to our Global Marketing teams, building the LTV, MMM, and CRM models that pinpoint exactly where Wise scales next. This role is about bridging the gap between deep technical research and real-world impact. By ensuring our model outcomes are robust, transparent, and—most importantly—actionable, you’ll turn complex data science into the insights that drive our mission and reach millions of customers worldwide. WHAT YOU’LL DO Drive Growth Strategy & Real Impact: Partner with Analytics, Marketing Channels Leads, and Finance to define the strategy for our marketing investments across the globe. You will be responsible for quantifying and communicating the business value and ROI of our data science initiatives, translating complex insights into actionable strategies for senior leadership. Technical Leadership & Innovation: Lead the research, experimentation, and rapid iteration in developing and evaluating advanced data science models for Marketing. This includes pioneering new approaches and refining existing methodologies for Marketing Mix Models (MMM), Customer Lifetime Value (LTV) modelling, and CRM modelling. You will driving innovation in how we build, validate, and deploy models that accurately predict customer behaviour, measure campaign effectiveness, and inform strategic marketing investments, directly contributing to Wise's growth Coach,Mentors & Scale: Lead, mentor, and grow the team building technical capabilities, fostering career development, and promoting knowledge sharing on cutting-edge technologies and methodologies. You will also be responsible for prioritising projects and allocating resources effectively within the data science team to ensure alignment with strategic objectives and timely delivery of impactful solutions.

WHAT YOU’LL BRING Technical Expertise: 7+ years hands-on experience. Strong technical foundation with expertise in coding (Python, SQL). You possess deep expertise in lifetime value (LTV) modelling and econometrics / marketing mix modelling, complemented by a strong understanding of statistics, particularly Bayesian reasoning, which enables you to accurately estimate results and know when to deliver actionable insights. Your technical toolkit includes experience with Bayesian approaches to machine learning, neural networks (ideally PyTorch), and a solid grasp of causal inference concepts, including their application with machine learning models. Furthermore, you are adept at navigating a diverse range of model types, confidently selecting between gradient boosting, neural networks, linear regression, or a blend, based on the specific problem and desired outcome. Leadership: 2+ years experience leading high-performing data science teams, driving the development of models in Marketing. Demonstrated ability to build, scale, mentor, and retain top technical talent, fostering collaborative and innovative team cultures. Domain Expertise: Experience in marketing operations and strategy with a deep understanding of the customer lifecycle from a marketing perspective. This includes expertise in customer acquisition, retention, engagement strategies, marketing campaign drivers, and the unique data challenges inherent in measuring marketing effectiveness, LTV, MMM, and CRM. Communication & Influence: Excellent communication skills with the ability to translate complex technical concepts into strategic business language and build consensus across diverse stakeholder groups. Strategic Ownership & Pragmatism: Demonstrated ability to proactively identify impactful opportunities, influence business strategy, and drive initiatives to completion. You possess a pragmatic approach, effectively triaging requests and adapting analysis scope to achieve optimal outcomes in a fast-paced environment. NICE TO HAVE BUT NOT ESSENTIAL An advanced degree (Masters / PhD) in Computer Science, Data Science, Machine Learning, Mathematics/Physics, or related quantitative fields preferred. Experience within Financial Services or FinTech