1 to 25 of 33 Propensity Modelling Jobs in the UK

transformation towards a subscription-led model, with Data Science becoming a critical part of future growth. What you'll be working on Subscription propensity modelling Churn prediction and retention analytics Customer segmentation and personalisation Dynamic pricing and offer optimisation Uplift modelling and causal inference Experimentation frameworks … theory Experience working with large-scale customer datasets Background in subscription, consumer, media, gaming, e-commerce, fintech or similar environments Experience with recommendation systems, propensity modelling, retention, personalisation or monetisation challenges would be particularly valuable. Founding hire into a growing Data Science capability Huge influence over future direction ...

data to understand audiences, inform fundraising decisions and generate genuinely actionable insight. You'll help develop areas such as audience profiling and segmentation, propensity modelling and lifetime value analysis, while ensuring existing reporting and data selections continue to be delivered effectively. The role would suit an experienced Insight … analysts, providing direction and helping develop the organisation's insight capability Move the function beyond reporting into more advanced analysis, including segmentation, audience profiling, propensity modelling and lifetime value Use tools such as SQL and Power BI to turn complex data into meaningful, actionable insight Work closely with ...

doing Lead the design, development, and delivery of analytical data products that support insight‐led business decisions. Examples may include customer value models, propensity models, performance measurement frameworks, forecasting solutions and self‐service insight products. Partner with Consumer Insights colleagues to combine behavioural, transactional and attitudinal research data into … Experience as a critical thought partner in defining analytical solutions, challenging assumptions constructively. Experience applying customer analytics techniques such as customer lifetime value modelling, segmentation, cohort analysis, propensity modelling or retention analysis. Proven experience in stakeholder‐facing analytics roles, with a track record of influencing decisions through ...

role if you bring: Professional experience in data analysis, business intelligence, analytics engineering or a closely related role. Strong experience of data preparation, cleansing, modelling and quality assurance. Experience using SQL and visualisation tools such as Power BI, Microsoft Fabric or equivalent platforms. Experience working with CRM, customer, commercial … Microsoft technologies. Familiarity with OpenClaw, Hermes or similar agent platforms. Track record of measuring AI, automation or digital‐product performance. Knowledge of predictive analytics, propensity modelling, segmentation or recommendation systems. Understanding of marketing attribution, customer journey analytics or revenue analysis. Ability to design trusted data products or reusable ...

quantitative field Preferred: experience taking initial machine learning models from exploratory notebooks to automated production pipelines Preferred: experience in customer behavior and propensity modeling, forecasting, optimization, or causal inference Core Competencies Demonstrates expertise in Data Science and Machine Learning Engineering, with a strong focus on developing production-ready models ...

CDPs (Salesforce Data Cloud, Adobe Experience Platform, Treasure Data, mParticle), MDM, CRM, and data warehousing Familiar with analytical approaches used in customer strategy: segmentation, propensity modelling, next‐best‐action, attribution, and customer journey analytics — ability to interpret and apply, not necessarily build Knowledge of cloud data platforms (Azure ...

CDPs (Salesforce Data Cloud, Adobe Experience Platform, Treasure Data, mParticle), MDM, CRM, and data warehousing Familiar with analytical approaches used in customer strategy: segmentation, propensity modelling, next-best-action, attribution, and customer journey analytics — ability to interpret and apply, not necessarily build Knowledge of cloud data platforms (Azure ...

CDPs (Salesforce Data Cloud, Adobe Experience Platform, Treasure Data, mParticle), MDM, CRM, and data warehousing Familiar with analytical approaches used in customer strategy: segmentation, propensity modelling, next-best-action, attribution, and customer journey analytics ability to interpret and apply, not necessarily build Knowledge of cloud data platforms (Azure ...

business spending, together. Please note: About the role Growth Intelligence owns the commercial data layer that powers Pleo's GTM engine — acquisition, retention, propensity modelling, and customer health. The team supports RevOps, Customer Experience, Customer Success, and senior commercial leadership with the data and models they need ...

business spending, together. Please note: About the role Growth Intelligence owns the commercial data layer that powers Pleo's GTM engine — acquisition, retention, propensity modelling, and customer health. The team supports RevOps, Customer Experience, Customer Success, and senior commercial leadership with the data and models they need ...

solved at both a tactical and strategic level. Experience delivering marketing analytics e.g., Marketing Mix Modeling , geo experiments, meta-analysis, audience segmentation and propensity modeling . Understanding of Bayesian approaches and modeling frameworks. Proven ability to generate practical solutions for marketing analytics problems and use results to drive business ...

payback rather than pieces mailed. Strong analytical judgement. You design test cells and holdouts, read results critically, and understand response and propensity modelling well enough to brief analysts, challenge their work and turn it into mailing decisions. Experience managing suppliers: tendering, negotiating, setting SLAs and keeping several partners … Source and buy prospect data from bureaus and brokers, and combine it with our first-party data. Direct the analysts building the response and propensity models that decide who we mail, with what offer and when. Work with risk analysts to shape offers around eligibility and lifetime value. Campaigns ...

Annalect team supports advanced use cases, helping clients customize their internal results to the tool, and apply advanced methods to enable better planning. Propensity modelling and activation – building and deploying algorithms that ensrue clients can reach their KPIs and targets in automated, streamlined way. Applied AI solutions – build ...

science, partnering with product managers, engineers and designers to embed data-driven decision making across the Engagement & Discovery squad. You will build churn and propensity models, perform deep analyses, and explore lifecycle drivers to guide acquisition, engagement and retention at scale. #J-18808-Ljbffr ...

positively impact customer retention and ecosystem growth by way of attach and lead pass opportunities Further embed our use of AI, data, and propensity models to develop proactive intervention capabilities that prevent customer churn Partner with enabling and delivery functions within Customer Success to implement retentions programs: CX Design ...

broken, you fix it. What You Will Do Decisioning & Data Science Design and implement NBA/NBO decisioning frameworks — eligibility rules, suppression logic, propensity score integration, offer prioritisation, and arbitration Operationalise propensity models, uplift models, CLV scores, and churn predictions into live decisioning frameworks Use value-based decisioning … decisioning solution — Pega CDH, Salesforce Marketing Cloud, Adobe Target/AEM/Campaign, Braze, Iterable, or purpose-built solutions Strong working knowledge of predictive modelling for decisioning: propensity, churn, CLV, uplift, and incrementality Experience designing statistically valid champion/challenger and multivariant tests and holdout methodologies Ability ...

broken, you fix it. What You Will Do Decisioning & Data Science Design and implement NBA/NBO decisioning frameworks — eligibility rules, suppression logic, propensity score integration, offer prioritisation, and arbitration Operationalise propensity models, uplift models, CLV scores, and churn predictions into live decisioning frameworks Use value-based decisioning … decisioning solution — Pega CDH, Salesforce Marketing Cloud, Adobe Target/AEM/Campaign, Braze, Iterable, or purpose-built solutions Strong working knowledge of predictive modelling for decisioning: propensity, churn, CLV, uplift, and incrementality Experience designing statistically valid champion/challenger and multivariant tests and holdout methodologies Ability ...

business spending, together. About the role Growth Intelligence owns the commercial data layer that powers Pleo's GTM engine from acquisition, retention and, propensity modelling to customer health. The team supports RevOps, Customer Experience, Customer Success, and senior commercial leadership with the data and models they need ...

measurement framework, quantify cross‐channel effectiveness, and isolate lift with causal inference methods. You’ll build production‐grade ML pipelines, apply segmentation and propensity models, and translate insights into concrete campaign recommendations. Collaboration with marketing, product, and engineering teams is essential. #J-18808-Ljbffr ...

role moves beyond traditional reporting to use data to understand audiences, inform fundraising decisions, and generate actionable insight. You'll develop audience profiling, segmentation, propensity modelling and lifetime value analyses, while ensuring current reporting remains strong. #J-18808-Ljbffr ...

analysts, developing an insight function that informs fundraising strategy and audience understanding. You will move beyond traditional reporting to advanced analysis, including segmentation, profiling, propensity modelling, and lifetime value analysis, while ensuring current reporting remains effective. #J-18808-Ljbffr ...

models in an organization from exploratory notebooks to reliable, automated production pipelines. Advanced Business ML (Experience in 1-2 of the following): Customer Behavior & Propensity Modeling: Building predictive models for churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions. ...

models in an organization from exploratory notebooks to reliable, automated production pipelines. Advanced Business ML (Experience in 1-2 of the following): Customer Behavior & Propensity Modeling: Building predictive models for churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions. ...

professional. Strong analytical and problem-solving skills, including the ability to create clarity from ambiguous business questions. Advanced Python skills for data analysis and modelling, together with strong SQL capability. Practical experience applying statistical and data-science techniques to business problems, including several of forecasting, regression, classification, segmentation … propensity modelling. Sound understanding of statistical concepts, model evaluation, uncertainty, bias and the limitations of analytical approaches. Experience working with large or complex datasets and performing data discovery, validation and exploratory analysis. Excellent requirements-gathering, stakeholder-management and project-planning skills. Excellent written communication, with experience producing analytical papers ...

subscribers - across viewing behaviour, curation, pricing and lifecycle - and turn what you find into action. Build models that inform decisions. Develop churn and propensity models, LTV estimates and behavioural segmentations that sharpen how we target, retain and delight subscribers. Put AI to work. Use tools like Claude Code … accelerate analysis and modelling, and help the team do the same. What you'll bring 3-5 years of experience in product analytics, product data science, or a similar role, ideally in a consumer subscription or digital product business. A strong grounding in data science methods - statistics, experimentation ...