At a Glance
- Tasks: Lead data science projects, build production-level code, and choose modelling approaches.
- Company: Fospha, a leader in online retail measurement solutions.
- Benefits: Competitive salary, career development framework, and collaborative team culture.
- Other info: Opportunity for mentorship and growth in a dynamic environment.
- Why this job: Make a real impact on marketing decisions for well-known brands.
- Qualifications: 3-5 years of data science experience, strong ML knowledge, and Python proficiency.
The predicted salary is between 65000 - 65000 £ per year.
Fospha is dedicated to building the world's most powerful measurement solution for online retail. For over a decade, we've helped teams make smarter decisions with full-funnel marketing insights, forecasting, and optimisation. With Fospha, every team moves faster and grows smarter.
About the role
We're looking for a Data Scientist to join Fospha's Data Science team in London. Fospha builds marketing measurement products for ecommerce brands — attribution, marketing mix modelling, incrementality testing, and brand impact measurement. Our Data Science team owns the models behind all of it, from methodology through to production code. You will work across our technical stacks and own maintaining and growing the codebases that power our product and solutions. This role suits an established data scientist who wants to own things properly. You'll lead larger production projects, choose the modelling approach rather than being handed it, and talk to clients as the modelling expert in the room — supported by a team that reviews each other's work seriously, and a company that rewards high agency with ownership.
What you'll do
- Lead larger production projects end to end — scoping, building, and shipping production-level code, rather than working through tickets someone else has specified.
- Choose the modelling approach — independently selecting and applying the right method within our suite, across attribution, marketing mix modelling, incrementality testing, and brand impact.
- Resolve bugs and queries independently — including in parts of the codebase you didn't write, without needing to route them upwards.
- Use AI as leverage, not as a crutch — solving coding tickets quickly, unblocking yourself on product and engineering dependencies, and building automation workflows that save the team time.
- Work with QA properly — using our automated tooling efficiently and flagging the gaps in it rather than working around them.
- Communicate as a modelling expert — confidently and independently, with clients and with colleagues, including when the message is that a number they like is wrong.
- Help develop the people around you — code review, methodology critique, and hands-on support for less experienced colleagues.
What we're looking for
Essential
- Solid commercial data science experience — typically 3–5 years, with a track record of shipping models into production.
- Strong ML knowledge across multiple algorithm families, and the judgement to pick the right approach for the problem rather than the one you know best.
- Strong Python and SQL, with the ability to lead on production-level code and set the standard others work to.
- Able to debug and resolve issues independently across unfamiliar repositories, using AI tooling to accelerate rather than to guess, while still understanding the problem fully.
- Strong AI fluency — you solve tickets quickly with it, you unblock cross-department dependencies with it, you build automation workflows with it, and you never send AI-assisted output without checking it.
- Confident, independent communication with clients and stakeholders as the technical authority on the work.
- Emerging mentorship — you're ready to develop junior colleagues, and you want to.
- Initiative in accepting and planning your own work, rather than waiting to be allocated it.
- Genuine attention to detail — much of this work involves noticing when a number is wrong.
Nice to have
- Bayesian modelling experience, particularly hierarchical models.
- Experience with AWS or comparable cloud tooling.
- Familiarity with automated QA tooling and test coverage practices.
- Experience with marketing, ecommerce, or advertising data.
Not required
- Experience with attribution methodology, MMM, incrementality testing, or Bayesian modelling is genuinely an advantage at this level — but it isn't a filter.
How you'll grow
We run a published Data Science Career Development Framework with six levels. You'd join at Career, where the expectations are:
- AI Fluency & Tooling: Leverages AI to solve coding tickets quickly; consistently unblocks themselves on cross-department dependencies, especially product and engineering; never sends AI-assisted communication without checking quality; expert at using AI to build automation workflows.
- Machine Learning & Modelling: Strong ML knowledge across multiple algorithm families; independently chooses and applies the right modelling approach within Fospha's suite.
- Engineering & Codebase: Leads on larger coding projects and tickets with production-level code; resolves queries and bugs efficiently and independently; master at using supplied AWS tooling through AI; uses automated QA tools efficiently and flags gaps to the QA team.
- Stakeholder & Communication: Confidently and independently communicates with clients and colleagues as a modelling expert; assists with the development of junior members of staff.
- Job Complexity: Consistent contributor and respected knowledge holder, with emerging collaboration and mentorship abilities.
- Supervision: Demonstrates initiative in accepting and planning work.
Progression to Advanced level is against explicit, published criteria — taking on almost any ticket efficiently, building and planning production repositories, collaborating with product on project outcomes and estimates, actively developing junior colleagues, and representing Fospha as a trusted external voice on modelling in senior client and partner conversations. You'll know what you're working towards from your first week.
Throughout, we look for the same core behaviours: concise communication, collaboration, problem solving, critical thinking, growth mindset, attention to detail, time management, and initiative.
Why Fospha
- Real methodological depth. Bayesian attribution, MMM, geo lift testing, and causal inference are our day job, not a side project.
- Ownership at the methodology level. At this level you're choosing the approach, not implementing someone else's choice.
- Published career framework. No guessing what the next level requires or when you'll get there.
- A team that reviews each other's work properly. Code review and methodology critique from people who care about getting the model right.
- Direct client impact. The models you build and maintain drive real budget decisions at brands you'll recognise.
Package
Salary: max £65,000
Location: London
Data Scientist employer: Fospha
Fospha is an exceptional employer that fosters a dynamic and inclusive work culture in the heart of London. As a Graduate Customer Success Coordinator, you will benefit from comprehensive training and mentorship opportunities, empowering you to grow your career while making a meaningful impact on client success. With a focus on data-driven growth, you'll be part of a collaborative team that values innovation and encourages proactive contributions.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Fospha!
✨Show Off Your Projects
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✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Fospha.
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We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Fospha, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Fospha. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Fospha
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Fospha!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
