Senior Data Scientist II

RELX Group
Charing Cross, United Kingdom
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Charing Cross, United Kingdom

Tech stack

API
Artificial Intelligence
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Business Analytics Applications
Data analysis
Big Data
Cloud Computing
Cloud Database
Customer Data Management
Information Engineering
Database Queries
Python
Machine Learning
Natural Language Processing
Oracle Applications
Cloud Services
Salesforce
Search Technologies
Systems Integration
Supervised Learning
Feature Engineering
Large Language Models
Snowflake
Prompt Engineering
Spark
Generative AI
Data Lake
People Soft
Machine Learning Operations
Databricks

Job description

We are seeking a Senior Data Scientist II who is a Data Science Generalist. The ideal candidate is comfortable working across GenAI, 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., * Build GenAI applications using OpenAI 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

Would you enjoy working on advanced machine learning models and cutting-edge analytics solutions?, This position is ideal for a versatile data scientist who enjoys solving diverse problems, working with multiple systems, and driving measurable business impact., * Strong Python programming skills.

  • Direct experience with OpenAI 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.

About the company

We are a fast-moving, high-impact Data Science & AI team building real-world GenAI and ML solutions across the entire LexisNexis business. Our work powers smarter decisions for Product, Sales, Finance, Marketing, Customer Success, and Engineering-everything from predictive models to enterprise GenAI apps to automation that transforms how teams operate. We are data science generalists who love variety. One day, it is designing a new GenAI 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.

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