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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist II - **Company:** LexisNexis - **Location:** Worcester, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Business Analytics Applications, Data Analysis, Big Data, Cloud Computing, Cloud Database, Customer Data Management, Information Engineering, Database Queries, Python (Programming Language), Machine Learning, Natural Language Processing, Oracle (Applications), Cloud Services, Salesforce.Com, Search Technologies, Systems Integration, Supervised Learning, Feature Engineering, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Generative AI, Data Lakes, People Soft, Machine Learning Operations, Databricks - **Published:** August 2, 2026 - **Apply:** https://www.apply4u.co.uk/jobs/x/42657841/ ## About the Role 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 organisation, 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 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. 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 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: 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. 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