Data Science Manager

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Overview

We're looking for a Data Science Manager who can turn commercial problems into scalable Data Science products. You'll work closely with the Director of Data Science to turn our Data Science strategy into scalable products and measurable business outcomes. You'll manage the day-to-day work of a growing team while partnering directly with stakeholders across the business to understand the decisions they're trying to make and identify where Data Science can materially improve the outcome.

This is a player-coach role. You'll lead and develop a growing team of Data Scientists while remaining close enough to the work to shape methodology, review technical outputs and contribute hands-on where needed. Your success won't be measured by how many models the team builds, but by how widely they're used and the commercial impact they create. This is not a research-focused role. We want someone who enjoys solving difficult modelling problems but is equally interested in what happens afterwards: how a model gets into production, how people use it, what we learn from it and whether it actually improves performance.

Responsibilities

• Commercial & Decision Science: Work with the Director of Data Science and business and marketing teams to identify high-value opportunities for Data Science, translating commercial problems into predictive models and decision-making products. You'll focus on areas including lifetime value, propensity modelling, customer value, forecasting and performance marketing optimisation, with commercial impact at the centre of how work is prioritised.

• Scalable Data Science Products: Guide the development of reusable models and methodologies that can scale across products, verticals and use cases rather than repeatedly solving the same problems. You'll take work from experimentation through adoption, ensuring outputs are measurable, explainable and designed around how they'll ultimately be used.

• Production Data Science: Work closely with Engineering and ML Ops to turn successful modelling work into reliable production products. You'll ensure models have appropriate experimentation, monitoring, retraining and feedback loops, while partnering with technical teams on the infrastructure needed to operate them at scale.

• Business Partnership & Insight: Build trusted relationships across Marketing, Commercial, Analytics, Product and Engineering. You'll translate business needs into Data Science opportunities and complex modelling into clear recommendations, ensuring the team goes beyond producing predictions to explain what they mean, what we can learn from them and what the business should do next.

• Team Leadership & Technical Quality: Lead and develop a growing team of Data Scientists while remaining technically close to the work. You'll coach the global team, review modelling approaches and technical outputs, establish consistent standards and contribute hands-on where it adds value. You'll create an environment where technical rigour, curiosity and commercial thinking reinforce one another.

Requirements

• Experience managing and developing Data Scientists while remaining comfortable working at a technical level.
• Strong experience in predictive modelling, machine learning and customer analytics.
• Experience applying Data Science to commercial or performance marketing problems, ideally including LTV, propensity, customer value or acquisition optimisation.
• Strong Python and SQL skills and experience working within modern cloud-based Data Science environments.
• Experience taking models beyond experimentation and working with Engineering or ML Ops teams to deploy and operate them in production.
• Strong understanding of experimentation and model evaluation, with experience in incrementality or causal inference particularly valuable.
• Excellent stakeholder management and communication skills, including the ability to translate complex technical work into clear recommendations for senior business audiences.
• A product mindset, with an interest in building reusable capabilities rather than one-off models.
• Strong commercial judgement and a focus on measuring the real-world impact of Data Science.

Nice to Have

• Experience in any of the following would be valuable, but we don't expect every candidate to bring all of them:
• Performance marketing and optimisation across Google, Meta or other major advertising platforms.
• Value-based bidding or conversion-value optimisation.
• Direct collaboration with advertising platform Data Science or Marketing Science teams.
• Affiliate marketing, lead generation or digital publishing.
• Financial services, insurance, health or other regulated industries.
• Attribution and marketing measurement.
• Forecasting commercial or marketing performance.
• DSP or advertising optimisation algorithms.
• Modern MLOps tooling and platforms such as Vertex AI.
• Applied AI techniques that complement traditional predictive modelling and decision science.

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Originally posted on Himalayas