Senior Marketing Data Scientist

LexisNexis Risk Solutions; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

LexisNexis Risk Solutions is seeking a Marketing Data Scientist to develop advanced analytical models that enhance marketing effectiveness and customer engagement. You will work with data across customer, behavioral, campaign and commercial datasets to build predictive models for propensity, segmentation, lead and account scoring, churn and next-best action.

You will collaborate with Marketing Analytics, MarTech and Data Engineering to operationalise scores and segments within CDPs, CRM and

Qualifications

• Strong experience in data science, predictive analytics or machine learning, ideally applied to marketing, customer or commercial use cases.

Responsibilities

• Develop predictive models across customer acquisition, engagement, conversion, retention and growth.
• Build propensity and lead/account scoring models to identify customers and prospects most likely to engage, convert, buy or expand.
• Develop advanced customer and account segmentation using behavioral, demographic, firmographic, engagement and commercial data.
• Build models identifying churn, retention and customer growth opportunities, supporting proactive marketing interventions.
• Develop next-best-action and recommendation approaches to improve customer journeys, targeting and personalisation.
• Apply statistical and machine-learning techniques to identify the customer behaviors, marketing activities and attributes that drive commercial outcomes.
• Design and evaluate A/B tests, multivariate experiments, control groups and incrementality studies to establish causal marketing impact.
• Monitor model accuracy and performance, including drift, bias and changes in underlying customer behavior, and recommend retraining or refinement where appropriate.

Skills

Python

SQL

Statistical Analysis

Machine Learning

A/B Testing

Marketing Analytics

Tools

scikit-learn

XGBoost

TensorFlow

Job description

Are you motivated to build scalable data solutions that directly shape customer engagement and marketing performance?

Do you enjoy collaborating across teams to turn complex data into actionable insights and real-time decision making?

About Our Team: The MarTech & Marketing Operations team brings together marketing technology, customer data, analytics and activation capabilities across LNRS Data Services.

We use data and advanced analytics to improve audience targeting, customer journeys, marketing effectiveness and commercial outcomes. Working across Marketing, MarTech, Data Engineering and commercial teams, we turn customer and behavioral data into intelligence that can be activated across marketing channels.

About the Role: As a Marketing Data Scientist, you will develop advanced analytical and predictive models that help Marketing better understand customers, identify growth opportunities and make smarter decisions across the customer lifecycle.

You will combine customer, behavioral, campaign and commercial data to build models for propensity, segmentation, lead and account scoring, churn, next-best action, personalisation and customer value.

Working alongside Marketing Analytics, MarTech and Data Engineering, you will take models beyond experimentation by helping integrate model outputs into the CDP, marketing automation and customer journeys, enabling insights to drive measurable marketing and commercial outcomes.

Responsibilities

• Develop predictive models across customer acquisition, engagement, conversion, retention and growth.
• Build propensity and lead/account scoring models to identify customers and prospects most likely to engage, convert, buy or expand.
• Develop advanced customer and account segmentation using behavioral, demographic, firmographic, engagement and commercial data.
• Build models identifying churn, retention and customer growth opportunities, supporting proactive marketing interventions.
• Develop next-best-action and recommendation approaches to improve customer journeys, targeting and personalisation.
• Apply statistical and machine-learning techniques to identify the customer behaviors, marketing activities and attributes that drive commercial outcomes.
• Design and evaluate A/B tests, multivariate experiments, control groups and incrementality studies to establish causal marketing impact.
• Work with Marketing Analytics to improve attribution and marketing effectiveness measurement, including understanding the incremental contribution of channels and campaigns.
• Partner with Data Engineering to define model-ready datasets, features and data-quality requirements.
• Partner with MarTech teams to operationalise scores, predictions, recommendations and segments within CDPs, CRM and journey orchestration platforms.
• Monitor model accuracy and performance, including drift, bias and changes in underlying customer behavior, and recommend retraining or refinement where appropriate.
• Translate complex modelling outputs into clear commercial recommendations that Marketing teams and senior stakeholders can act upon.
• Champion experimentation and responsible use of AI and advanced analytics within Marketing.

Requirements

• Strong experience in data science, predictive analytics or machine learning, ideally applied to marketing, customer or commercial use cases.
• Advanced proficiency in Python and SQL, with experience working with large customer, behavioural and transactional datasets.
• Strong knowledge of statistical and machine-learning approaches including regression, classification, clustering, propensity modelling and predictive scoring.
• Experience developing marketing or customer models such as propensity to buy, lead scoring, churn prediction, customer segmentation or customer lifetime value.
• Strong understanding of experimental design, A/B testing, causal inference and incrementality measurement.
• Experience with machine-learning libraries and frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow.
• Experience working with cloud data and analytics platforms such as Azure, Databricks, AWS or GCP.
• Understanding of marketing data ecosystems including CDPs, CRM, marketing automation, digital behavioral data and journey orchestration.
• Experience collaborating with data engineering teams to create features, training datasets and production scoring pipelines.
• Ability to evaluate models using appropriate measures such as precision, recall, ROC-AUC, lift and calibration, selecting metrics appropriate to the commercial use case.
• Strong understanding of data governance, privacy, GDPR and responsible AI, particularly when using customer data.
• Excellent communication and data-storytelling skills, with the ability to translate sophisticated analytical work into clear marketing and commercial decisions.