Senior Data Scientist

We’re the Moonpig Group – home to Moonpig, Greetz, Red Letter Days and Buyagift – and we’re on a mission to make people feel loved, celebrated and remembered. Whether it’s a card that gets them laughing out loud or a gift that makes their day, we help people stay close, no matter the miles.

We’re proud to be leading the online gifting revolution, with brilliant products, clever tech and a whole lot of heart. Our platform makes it easy to create moments that matter – packed with personal touches and delivered with care.

We’re not just about selling cards or gifts – we’re here to spread joy, spark smiles and make every celebration feel extra special. And with values that guide how we work and support one another, we’ve built a place where people (and ideas) can truly thrive.

If you’re looking to make an impact, bring your spark and be part of something meaningful – we’d love to have you on the team. 🌙🐷

Senior Data Scientist | 📍London or Manchester – Hybrid (1–2 office days per week) | 💰Competitive Salary + Benefits

About the Role

We’re looking for a Senior Data Scientist to join Moonpig, working hybrid from London or Manchester. You’ll own high-value machine learning problems end-to-end, from identifying opportunities and shaping problems through to technical delivery, production and measurable customer or commercial impact.

This is a hands-on senior individual-contributor role with significant technical and product ownership. You’ll work across recommendations, personalisation, customer modelling and predictive modelling, partnering closely with Product, Engineering, MLOps, Commercial and Marketing to understand where Data Science can create the most value and how we should measure success.

You’ll have the space to navigate ambiguity and make sound technical decisions independently. You’ll design robust offline and online evaluation, own meaningful models and ML components throughout their lifecycle, and use evidence to help shape product and business decisions.

\n

Key Responsibilities

• Own machine learning problems, models and components end-to-end across recommendations, ranking, personalisation, customer modelling and predictive modelling.

• Partner with Product, Commercial, Marketing and other stakeholders to identify high-value opportunities, shape ambiguous problems and determine whether Data Science is the right intervention.

• Independently select, build and improve modelling approaches, using feature engineering, tuning and appropriate algorithmic choices to improve performance.

• Design robust offline evaluation strategies, selecting metrics that reflect problem-specific behaviour and trade-offs rather than relying solely on generic model-performance measures.

• Design and support online experiments to evaluate real-world impact, working with Product and Analytics partners to define success metrics, guardrails and appropriate interpretation of results.

• Own outcomes beyond model delivery: follow solutions through production and experimentation, determine whether they are creating the intended customer or commercial impact and drive iteration where they are not.

• Design components of machine learning systems, such as feature-generation pipelines, model-scoring logic and inference workflows, working closely with Engineering to integrate solutions into production.

• Collaborate with MLOps to deploy models and ensure appropriate monitoring, retraining and operational processes are in place, addressing issues such as drift, data-quality problems and performance degradation.

• Write high-quality, tested and maintainable Python and SQL, contributing robust and reproducible solutions to shared production codebases.

• Build practical AI-powered features where appropriate, such as solutions using prompts, embeddings or other generative AI capabilities, and evaluate their outputs systematically.

• Use AI-assisted development tools to improve coding, analysis, experimentation and documentation, critically evaluating outputs and identifying opportunities to improve team workflows.

• Communicate technical decisions, model behaviour, trade-offs and recommendations clearly, using evidence to influence product and business decisions and prioritisation.

• Contribute to the wider Data Science capability through informal mentorship, peer review, knowledge sharing, reusable tooling and improvements to technical practices and ways of working.

About You

• Strong experience developing and delivering machine learning solutions in a Data Science, Machine Learning or closely related role, including ownership of models or substantial ML components.

• Strong practical understanding of supervised machine learning, feature engineering, model selection, tuning, validation and evaluation, with experience independently improving model performance.

• Strong Python and SQL skills, with experience developing robust Data Science solutions in shared production codebases.

• Ability to take ambiguous customer or business problems, determine an appropriate Data Science approach and independently drive the work through to a usable solution and understood outcome.

• Strong experience designing offline evaluation approaches and selecting metrics that appropriately reflect model performance and problem-specific trade-offs.

• Experience designing, analysing and interpreting online experiments, with the ability to connect technical model performance to customer and commercial outcomes.

• Experience deploying or contributing significantly to the deployment of machine learning models, with a good understanding of monitoring, retraining, data quality, model drift and common production issues.

• Experience designing components of machine learning systems, such as feature pipelines, scoring logic or model workflows, and working effectively with Engineering to integrate them into production.

• Strong understanding of testing, version control, reproducibility and maintainable software-development practices within a Data Science environment.

• Able to make sound technical decisions independently and clearly articulate the trade-offs between alternative modelling, evaluation and implementation approaches.

• Able to communicate complex technical concepts, assumptions and recommendations clearly and use evidence to influence Product, Engineering and business stakeholders.

• Effective use of AI-assisted development tools to improve coding, analysis and experimentation, combined with strong judgement around validation, privacy, security and responsible use.

• Strong awareness of data quality, privacy, fairness, security and customer-experience considerations when designing and deploying machine learning solutions.

• Experience developing recommendation, ranking or personalisation systems would be beneficial.

• Experience with customer modelling approaches such as propensity, uplift or customer lifetime value modelling would be beneficial.

• Experience applying LLMs, embeddings or other generative AI techniques to build practical product or Data Science features would be useful.

• Experience working in a B2C e-commerce, retail or other high-volume digital product environment would be useful.

• Experience working with cloud-based machine learning infrastructure and services, particularly AWS, would be beneficial.

• Familiarity with analytics engineering tooling such as dbt would be useful.

• Experience with large-scale or near-real-time ML systems would be beneficial.

• Experience mentoring junior team members, supporting peer review, sharing knowledge and acting as a key source of technical support would be beneficial.

• A degree in Statistics, Mathematics, Economics, Computer Science or another relevant quantitative discipline can be helpful, but equivalent practical experience is equally welcome.

Our Tech Environment

• Python and SQL for developing robust Data Science solutions.

• AWS for cloud-based machine learning infrastructure and services.

• ML systems spanning feature-generation pipelines, model-scoring logic and inference workflows.

• Production ML practices covering deployment, monitoring, retraining, data quality and model drift.

• Offline evaluation and online experimentation to connect model performance with customer and commercial outcomes.

• Generative AI capabilities including prompts, embeddings and other practical AI approaches.

• AI-assisted development tools across coding, analysis, experimentation and documentation.

• dbt within our wider analytics engineering tooling.

How We Get There You’ll own meaningful, sometimes ambiguous Data Science problems from end to end: shaping the problem, deciding on the right approach, getting solutions into production and designing how their impact will be evaluated.

Success isn’t simply about building a strong model. It’s about understanding whether Data Science is the right intervention in the first place, making thoughtful trade-offs between performance, complexity and maintainability, and demonstrating whether the resulting solution improves customer or commercial outcomes.

You’ll use evidence to influence decisions and prioritisation across Product, Engineering and the wider business. Alongside your own delivery, you’ll help strengthen our Data Science capability through peer review, informal mentorship, reusable approaches, knowledge sharing and improvements to our technical ways of working.

Interview ProcessFollowing an initial recruiter screening, the expected process includes a Hiring Manager Interview, Technical Screening, Technical Interview Follow-up and Final Round.

The exact structure is still being confirmed, and we’ll keep candidates informed of any changes throughout the process.

\nWhat's in it for you?

We believe in empowering our team to do their best work. Enjoy:

💰 Competitive Pay & Bonuses: Plus, generous pension plans & staff discounts.

💆🏽 Wellbeing First: Private healthcare (UK) and mental health support

🏖️ Flexible Working & Time Off: Generous holidays, hybrid working (1-3 days in office, depending on role/team) & up to 20 days of international working.

📈 Career Growth: Learning allowances, coaching & development programs.

Want to know more?

Explore our full benefits package: here

Check out our podcast, tech blog and product blog to hear more about how we work and what we're building!

Our Ways of Working:

We trust our colleagues to do what’s right and offer flexibility to support a balance between work and life. At the same time, face-to-face office time is an important and expected part of working at Moonpig Group. We believe regular in-person working supports collaboration, alignment, and effective decision-making. Candidates will have regular and ongoing time working from the office as part of their role, which will be discussed during the recruitment process.

Moonpig Group's Commitment to Equality, Diversity, and Inclusivity:

At Moonpig Group, we’re all about creating a workplace where everyone feels they truly belong. We celebrate what makes each of us unique, whether that’s our background, how we work best, or what matters most to us.

From working parents who need flexible hours to neurodiverse colleagues with specific working styles, we’re here to support our people in ways that work for them. Because when you feel valued and included, you can thrive, and so can we.

We’re proud to have a number of employee-led groups driving this forward, including our LGBTQ+, Gender Balance, Neurodiversity and EMBRACE (Educating Myself for Better Racial Awareness and Cultural Enrichment) communities, plus our Group-wide EDI committee. These teams help make sure every voice is heard and every idea has a place.

We know that diversity fuels creativity, innovation and connection, and that’s why we’ll keep pushing for progress. Together, we’re building a culture where everyone feels safe, supported, and free to be their brilliant, authentic selves.

If you have a preferred name, please use it to apply and share your pronouns if you are comfortable to do so😊 - If you have any reasonable adjustment requests throughout the interview process please let us know on your application or speak to the Recruiter.