Lead Data Scientist - Marketing CRM

Wise•4h ago

United KingdomOnsiteFull-timeSenior Level5+ yrs exp

Apply now

Top focus

Data ScientistVp Data

Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed. Whether people and businesses are sending money to another country, spending abroad
making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money. For everyone, everywhere. More about our mission and what we offer .
We’re looking for a Lead Data Scientist to join our growing Marketing - CRM Team in London. This role will give you the opportunity to have a direct impact on how millions of Wise customers find the perfect products for their needs at exactly the right moment
evolving your skills by owning production-level machine learning projects from end to end. This is an individual-contributor (IC) role — you'll own technical direction and delivery, not people management. Your mission: Wise has pioneered new ways for people to move and manage money across borders. Once someone joins Wise, our CRM tribe makes sure every customer discovers the products that actually help them — a card if they travel, multi-currency balances if they hold FX, a current account if they use Wise for day to day banking. Your mission is to develop a learning system that recommends the right product, to the right customer, at the right moment — and measures whether it worked. Here’s how you’ll be contributing to CRM: You will design and maintain the data pipelines and ML-ready tables that power feature generation, model training, inference and impact measurement – with clear ownership of data quality, reproducibility and training-serving consistency. You will help the CRM tribe find the biggest opportunities for growth and personalisation across the customer lifecycle — onboarding, engagement, activation, retention. You will own predictive/uplift or recommendation models that decide what to recommend and who to talk to, powering personalised CRM journeys end-to-end. You will model customer behaviour, product-usage patterns and CRM engagement (email/push clicks) to identify who's likely to churn, who's ready for their next product
measure how much value a campaign actually created. You will partner closely with Data Analysts, Data Engineers and CRM Campaign Managers, translating models into levers they can use. Your average day will include building or maintaining production models, running experiments, evaluating new ideas
communicating what models can (and cannot) tell us about how and why CRM drives growth. This role will give you the opportunity to: Have a direct impact — you will closely partner with the CRM and CRM Analytics team to help ship models that reach every Wise customer through CRM campaigns and personalisation surfaces. Own the problems worth solving — you will not just be handed a backlog. You will form strong opinions on where DS creates the most value and convince the people around you to do what is necessary to help. Work autonomously - we believe people are most empowered when they can act autonomously. So rather than telling you what to do, you’ll work with your team to create a vision of your own. Of course, you can always gather feedback from smart, curious people across Wise but you’ll have the freedom to make your own calls. Be part of a diverse team - You will work in a team of Data scientists, Analysts, Engineers and CRM Managers. Be part of our mission to make money without borders the new normal About you: You have expert knowledge of Python and are able to make and justify design decisions in your code (packaging, testing, config-driven pipelines). You have expert knowledge of SQL and can write, debug and optimise complex queries against a warehouse. You have hands-on experience shipping and operating predictive ML models or recommendation systems in production (classification / propensity / churn / next-best-action) — end-to-end from feature pipelines (SQL or feature store) and ML-ready training / scoring datasets, through model registries, batch or real-time inference
monitoring — with sound data-modelling practices and data-quality checks along the way. You know when to reach for gradient boosting, neural networks, linear models
a blend. You have designed and analysed A/B tests end-to-end — power calculations, choosing the right unit of randomisation, guardrail metrics, interpreting inconclusive results. You have a proficient understanding of statistics — sample size, variance, multiple-comparisons, Bayesian reasoning. You know how to measure the incremental impact of ML models and CRM interventions end-to-end — defining success and guardrail metrics, designing and powering A/B tests, choosing the right unit of randomisation, quantifying uncertainty, accounting for multiple comparisons
interpreting inconclusive results to inform business decisions. You take end-to-end ownership with a structured, data-driven approach — cutting through vagueness to frame the business problem, prioritising the value you can add
defining precisely where and how a model fits into the stack. You communicate effectively with any audience — translating model choices, uncertainty and trade-offs into decisions non-DS teams (Product, Marketing, CRM Ops, Compliance) can act on
visualising data clearly along the way. You operate beyond your direct team — shaping design decisions on the product or engineering side, mentoring other DS and analysts, spawning and running cross-functional projects through to delivery, educating the wider company on what DS can do
keeping current with developments in your areas (recommenders, uplift, causal inference). Some extra skills that are great (but not essential): You have hands-on experience with sequential and/or deep-learning recommender models — two-tower architectures, transformer-based rankers
sequence embeddings. You have experience with uplift modelling and conditional average treatment effects for personalisation. You are familiar with dbt, Snowflake, AWS (S3, SageMaker), Airflow
Trino / data lake stacks. You have prior CRM / marketing / e-commerce ML experience — customer lifecycle segmentation, campaign attribution, contact strategy optimisation. Office: London, UK Salary range: GBP 90.5K-127K yearly gross based on experience and interview outcomes* Key benefits: Flexible working - whether it’s working from home, school plays or life admin we get that flexibility is essential and you’re trusted to do the right thing and be responsible Stock options in a profitable company Relocation support Generous parental leave Pension scheme Paid sabbatical Loads of development opportunities Sports and wellbeing compensation A fun work environment with social activities and events The opportunity to work with super smart, curious people
We’re people without borders — without judgement or prejudice, too. We want to work with the best people, no matter their background. So if you’re passionate about learning new things and keen to join our mission, you’ll fit right in. Also, qualifications aren’t that important to us. If you’ve got great experience
you’re great at articulating your thinking, we’d like to hear from you. And because we believe that diverse teams build better products, we’d especially love to hear from you if you’re from an under-represented demographic. For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive. We're proud to have a truly international team
we celebrate our differences. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers. If you want to find out more about what it's like to work at Wise visit Wise.Jobs . Keep up to date with life at Wise by following us on LinkedIn and Instagram .

Required skills

PythonSnowflakedbtAirflowTransformersAWSS3