Senior Data Scientist II in London

About our team

We are a fast-moving, high-impact Data Science & AI team building real-world Gen AI and ML solutions across the entire Lexis Nexis business.

Our work powers smarter decisions for Product, Sales, Finance, Marketing, Customer Success, and Engineering—everything from predictive models to enterprise Gen AI apps to automation that transforms how teams operate.

We are data science generalists who love variety.

One day, it is designing a new Gen AI workflow, the next it is deploying a model into Salesforce or engineering a pipeline in Databricks.

We own our projects end-to-end and partner directly with stakeholders to deliver solutions that get used and make a measurable difference.

If you want to experiment, build, ship, and see your work drive real impact across a global organization, you will feel right at home with us.

About the role

We are seeking a Senior Data Scientist II who is a Data Science Generalist.

The ideal candidate is comfortable working across Gen AI, traditional machine learning, analytics, data engineering, cloud platforms, and enterprise system integrations.

In this role, you will design, build, and deploy AI and ML solutions that support key business functions across Product, Sales, Finance, Marketing, Customer Success, and Engineering.

You will work end-to-end across ideation, modelling, experimentation, prompt engineering, deployment, monitoring, and stakeholder communication.

This position is ideal for a versatile data scientist who enjoys solving diverse problems, working with multiple systems, and driving measurable business impact.

Responsibilities

• Build Gen AI applications using Open AI APIs, embeddings, vector search, and retrieval-augmented generation (RAG).
• Design advanced prompt engineering patterns and automated evaluation frameworks for LLM quality and safety.
• Develop and deploy traditional ML models (e. g., churn, propensity, sentiment/feedback, lead scoring, customer intelligence).
• Own the end-to-end model lifecycle: data prep, experimentation, deployment, and monitoring.
• Build and optimize feature pipelines and scoring jobs using Python, Databricks, Spark, Delta Lake, and AWS.
• Use AWS services (S3, Redshift, Lambda) for data automation, orchestration, and scalable processing.
• Ensure data quality, observability, lineage, and documentation across data and ML pipelines.
• Deliver enterprise integrations with Salesforce (SFDC) and Oracle platforms (Fusion, Service Cloud, Peoplesoft) for batch and real-time workflows.
• Create analytics solutions with cross-functional partners: define KPIs, connect customer/product/finance/CRM data, and drive actionable recommendations.
• Productionise reliably: provide L2/L3 support, monitor drift/data quality/prompt performance, run root‑cause analysis, and implement preventative fixes.

Requirements

• Strong Python programming skills.
• Direct experience with Open AI APIs, LLM workflows, and prompt engineering.
• Solid machine learning fundamentals, including supervised learning, NLP, and feature engineering.
• Experience with Databricks, Spark, and Delta Lake.
• Strong SQL skills with experience working on large datasets.
• Experience with AWS, including S3 and Lambda.
• Familiarity with Redshift, Snowflake, or other cloud data warehouses.
• Experience with behavioral datasets.
• Ability to work across machine learning, data engineering, analytics, and integrations.
• Ability to design end-to-end solutions spanning data, models, APIs, and automation workflows.
• Equal Opportunity Employer

We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

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