Data Scientist - UK
Focal Systems
| Company | Focal Systems |
| Category | Data & Analytics |
| Location | Remote |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 8 Jun 2026 |
| Last verified | 2 Aug 2026 |
| Source | Employer career page (greenhouse) |
Description
Type: Full-time, salaried employment Location: Remote in the UK only. Salary Range: £60,000 to £70,000 + stock options Focal Systems is the industry leader in retail AI solutions. We are headquartered in San Francisco, California with operations in Canada and the UK, and a tech-hub in Poland. We are a Deep Learning first company. Our mission is to automate and optimize brick and mortar retail using deep learning computer vision. Focal Systems has been deployed at scale with some of the top retailers in the world.
We seek smart, creative and passionate people who want to help build a great and enduring company and deploy Deep Learning to the world!
About the Role
As a Senior Data Scientist on our Analytics team, you will own end-to-end problems that combine applied statistics, experimentation, and machine learning to drive measurable improvements in how our retail AI products perform in the real world. You will work on ambiguous, open-ended questions, define the right approach, and deliver insights and models that influence the roadmap of Product, Engineering, and Operations.
This is a senior individual contributor role. You will set the technical direction for your projects, partner with cross-functional stakeholders, and act as a force multiplier for analytics quality across the company.
What We Are Looking For
You are a rigorous thinker who treats data as evidence, not decoration, and is comfortable telling a clear story about uncertainty
You have strong technical chops in SQL and Python, and you reach for the right tool, statistical, experimental, or ML, based on the problem in front of you
You can design experiments, reason about causality, and identify when a correlation is enough and when it is not
You are comfortable owning ambiguous, open-ended problems and breaking them down into a credible plan
You communicate findings crisply to technical and non-technical audiences, in writing, in dashboards, and in person
You raise the bar for the team through code review, methodology review, and mentorship of less senior analysts and scientists
What You Will Be Doing
Own analytical problems end to end from problem framing and metric definition through data acquisition, modeling, validation, and stakeholder rollout
Build predictive and inferential models such as forecasts, propensity and uplift models, and survival or duration analyses, where they meaningfully improve a product or operational decision
Develop statistical methods and reusable analyses for measuring performance, including detection accuracy, intervention impact, and downstream operational outcomes
Partner with Product, Engineering, and Operations to define key metrics, implement new features, and translate business questions into well-scoped analytical work
Audit system outputs and provide structured feedback that improves product accuracy and reliability
Communicate findings through dashboards, written analyses, and stakeholder presentations that lead to concrete decisions
Help define best practices around data quality, experimentation, modeling, and reproducibility, and contribute to the team’s data contracts and SOPs
Requirements
5+ years of experience as a Data Scientist, Quantitative Analyst, or similar role, with a track record of shipping analyses and models that changed product or business decisions
Strong proficiency in SQL, including complex CTEs, window functions, and query optimization
Strong proficiency in Python for data work, including pandas, NumPy, and experience with statsmodels or other relevant tools
Solid grounding in applied statistics: hypothesis testing, regression, experimental design, and uncertainty quantification
Experience designing and analyzing A/B tests or other controlled experiments in a product setting
Experience building at least one class of predictive model end to end, for example forecasting, classification, or propensity
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