13 of 13 Permanent Predictive Modelling Jobs in the Thames Valley

and Python skills Experience with data visualisation tools (Power BI preferred, but flexible) Proven background in analytics or data science, ideally in a commercial or operational context Exposure to predictive modelling, segmentation, or pricing analytics Ability to work independently and communicate insights clearly to non-technical stakeholders WHY APPLY? Play a central role in shaping analytics for one More ❯

role in their data-driven journey, working across customer, marketing, commercial, geospatial, and operations datasets to uncover insights that matter. What you’ll do: Lead advanced analytics projects, including modelling, forecasting, and deep-dive insights Translate complex data into actionable recommendations for business leaders Work across diverse datasets – from customer journeys to operational performance and geospatial intelligence Partner with … driven sustainability strategies Drive innovation in how we use analytics to achieve commercial and environmental impact Experience required: Strong experience in SQL and Python for data manipulation, analysis, and modelling Proven background in advanced analytics, forecasting, or predictive modelling Ability to connect data with business strategy, delivering clear and actionable insights Experience with commercial, marketing, or operational More ❯

The successful Lead Data Analyst responsibilities will include: Lead end-to-end analytical projects across pricing, promotions, and customer segmentation, delivering insights that drive commercial performance. Develop and deploy predictive models to forecast demand, optimise price elasticity, and measure promotional effectiveness. Translate analytical outputs into clear, actionable recommendations for commercial, marketing, and finance stakeholders. Partner with senior leaders to … Analyst will have the following skills and experience: Strong technical skills in SQL and Python, with experience in building models for pricing, forecasting, or customer segmentation. Proven experience applying predictive modelling and statistical techniques to solve commercial challenges. Excellent stakeholder management - able to work autonomously, prioritise effectively, and communicate complex insights with clarity. A proactive, ownership-driven mindset More ❯

future, not maintain the past. They are looking for a passionate and experienced Senior Data Scientist/Manager to lead personalization efforts within their CRM ecosystem. You’ll develop predictive models and recommendation systems that enhance customer engagement across global markets. THE ROLE Lead development of machine learning solutions for CRM personalization. Build and optimize recommendation engines using neural More ❯

/ML Implementation: Design and implement AI/ML algorithms to automate and enhance geological interpretation. Develop AI/ML algorithms to improve geological realism in subsurface models. Validate predictive models using blind test datasets and real-world scenarios. Work closely with colleagues from multiple disciplines to refine and enhance workflows. Stay current with AI/ML and geoscience More ❯

Harmsworth Media (the I Paper and the New Scientist). You will define and maintain reports to track key performance indicators (KPIs), build advanced customer segmentation models, and leverage predictive analytics to enhance business decision-making and identifying key insights to help identify audience opportunities for prospecting. Main Responsibilities Lead data analysis initiatives across the subscriber lifecycle, from acquisition … actionable insights to stakeholders across the business. Develop, improve, and maintain customer segmentation models to provide deep insights into the subscription base and inform Product/Marketing strategies. Utilise predictive analytics and retention modelling to forecast churn risk and customer behaviour, leveraging data science techniques. Collaborate with cross-functional teams , including marketing, finance, product and editorial, to ensure … a subscription-based business . Strong commercial acumen , with the ability to translate data insights into business strategies. Advanced analytical and quantitative skills , with expertise in statistical analysis and predictive modeling. Proficiency in SQL, Python, or R , and experience with data visualization tools such as Power BI, Tableau, or Looker Studio. Excellent stakeholder management skills , with the ability to More ❯

projects. AI & Machine Learning: Embed scalable ML/AI capabilities into the data infrastructure to enhance applications and business processes. Promote adoption and support high-value initiatives such as predictive modelling, advanced analytics, and intelligent automation. Data Governance & Compliance: Chair the Group-wide Data Governance Committee to enforce standards, mitigate risks, and ensure ethical data use. Supplier Management More ❯

centric mindset with a deep understanding of shopper behaviour and journey mapping Data & Analytics Expertise • First-party data utilization for targeting, personalization, and measurement • Closed-loop attribution and ROI modelling • Advanced analytics and performance optimization using dashboards, A/B testing, and predictive modelling AdTech & Product Knowledge • Familiarity with ad platforms (DSPs, SSPs, etc) • Experience with sponsored More ❯

and delivery of data-driven programmes across R&D, clinical, regulatory, and commercial functions. AI & Advanced Analytics: Drives the design and implementation of AI/ML solutions to enable predictive modelling, real-world evidence generation, clinical trial optimisation, and personalised medicine strategies. Oracle Platform Expertise: Oversees the implementation, integration, and optimisation of Oracle-based data platforms to ensure More ❯

Key responsibilities include: Developing and owning the change strategy for assigned product areas Collaborating with cross-functional teams to deliver change initiatives Leading the integration of optimisation tools and predictive models into workflows Driving user adoption through structured engagement and training Defining KPIs and success metrics to measure change impact The ideal candidate will have: Proven experience in product More ❯

charge point management systems (CPMS), telemetry, and CRM tools. Optimise Site Performance: Identify underperforming sites and propose actionable improvements related to energy supply, pricing, location, or equipment configuration. Data Modelling & Forecasting: Build predictive models to forecast demand growth, charging behavior, and energy costs to support investment decisions and DNO capacity planning. Revenue & Cost Analysis: Analyse tariff structures, grid … Desirable: Experience with energy pricing , load balancing , or DNO connection capacity data . Knowledge of EV charging infrastructure , OZEV grant schemes , or CPMS platforms. Familiarity with machine learning or predictive analytics for demand forecasting. Degree in a relevant field (Data Science, Engineering, Economics, or similar). More ❯

candidate will contribute to the development and execution of the credit risk roadmap and assist in prioritising tasks to ensure successful delivery. Key Responsibilities Model Development and Implementation Develop predictive models across key areas such as credit decisioning, fraud detection, and customer segmentation. Ensure data integrity and quality through robust validation and auditing processes. Evaluate and document scorecard effectiveness … and conduct experiments to test hypotheses and inform decision-making, defining expected outcomes and tracking results. Analyse processes and customer journeys to identify measurable improvement opportunities. Highlight areas where modelling can create uplift in decision quality, efficiency, or cost-effectiveness. Investigate new data sources and methodologies to enhance customer decisioning and experience. Controlling Credit Risk Propose, monitor, and analyse More ❯

to the development and execution of the credit risk team’s roadmap and helping to prioritize tasks to ensure that delivery is met. RESPONSIBILITIES Model Development and Implementation Developing predictive models for high impact areas, e.g. credit decisioning, fraud detection, and customer segmentation. Ensure data integrity and quality through validation and auditing processes. Evaluation of scorecard effectiveness, with accompanying … defining expected outcomes and tracking against these. Understand wider process and customer journeys, allowing identification for potential improvements that can be quantified through analysis or tests. Identify areas where modelling can create uplift in decisions, efficiency, and costs. Explore new data sources and methodologies to improve the customer journey and decisions. Controlling Credit Risk Propose, monitor, and analyze credit More ❯