8 of 8 Remote Statistical Modelling Jobs in Central London

Design and deploy machine learning models for risk scoring, customer segmentation, and product optimisation Analyse large financial datasets to uncover trends and support product and credit teams Develop custom statistical models for portfolio analysis and performance forecasting Collaborate cross-functionally with engineering, product, and analytics teams Contribute to scalable data pipeline and feature store development Requirements: 3+ years of More ❯

of the country’s largest retailers plans and executes promotions. In this high-impact role, you’ll help shape the future of promotional planning through cutting-edge analytics, advanced modelling, and customer-first insights that drive tangible change at scale. 🚀 What You’ll Be Doing: Design and deploy data science models for promotional strategy and price optimisation. Work closely … to at least degree level in Statistics, Mathematics, Machine Learning, Computer Science, Chemistry, Physics, Economics or related field. Advanced skills Python and SQL (Pyspark a bonus). Experience with statistical modelling and machine learning workflows. Confidence working across a full data science tech stack—pipelines, models, cloud infra. A collaborative mindset and strong stakeholder communication skills. 🌍 Why Join More ❯

science methods for measuring the quality of health and social care services, learning from best practice (national/international), both internally and externally. Assess the effectiveness of different advanced statistical and data science modelling approaches and advise data scientists on best tools and approaches to support organisational commitments. Manage competing demands for Data Science work within the Data More ❯

science methods for measuring the quality of health and social care services, learning from best practice (national/international), both internally and externally. Assess the effectiveness of different advanced statistical and data science modelling approaches and advise data scientists on best tools and approaches to support organisational commitments. Manage competing demands for Data Science work within the Data More ❯

a scalable, tech-enabled platform that intercepts millions of unwanted items every year, transforming them from landfill-bound to re-loved by new owners. Responsibilities: Lead and deliver scalable statistical models and analyses that drive business decisions Own end-to-end delivery of data science projects, from identifying and integrating data sources to experimentation, model deployment, and pricing strategy … optimization Define and implement best practices in modelling, automation, and process improvements, ensuring the right tools and approaches are used for each problem. Effectively communicate complex insights to both technical and non-technical audiences, including senior executives, with a strong focus on commercial impact. Collaborate cross-functionally with product managers, engineers, and other stakeholders to align data science work More ❯

FMCG. They pride ourselves on nurturing talent and fostering a culture of innovation and inclusion. As a Senior Consultant, you will: Lead projects to deliver actionable insights using advanced statistical modeling and research integration. Drive business development , contributing to proposals and pitches. Collaborate on product development and innovate new analytical approaches. Work with diverse datasets, ensuring quality control and More ❯

calibre of people they employ and will do everything to support you and your development. Role Overview: As a Data Scientist you will be hands on in developing advanced statistical and machine learning models within the financial sector. Python is obviously a pre-requisite for this role, with experience required in processing and model development. You will develop end More ❯

the quality of insights depends on the quality of the data. 10. You understand that models are only as good as their assumptions. You can critically assess epidemiological and statistical models, ensuring biases and confounders are accounted for. You’re comfortable with methodologies like propensity score matching, inverse probability weighting, and instrumental variable approaches. 11. You don’t just … and limitations of each data source. 14. You know when a real-world study is the answer—and when it’s not. You can identify when another approach (e.g., modelling, meta-analysis, or a new trial) is needed. 15. You believe in impact, not just publications. Publishing is great, and you’ve done it—but what excites you is More ❯