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- Tasks: Build and deploy AI and ML solutions that drive real business value.
- Company: Join a fast-moving Data Science & AI team at LexisNexis.
- Benefits: Enjoy country-specific benefits and a supportive work environment.
- Other info: Collaborative culture with opportunities for professional growth.
- Why this job: Make a real impact with cutting-edge GenAI and machine learning projects.
- Qualifications: Strong Python skills and experience with OpenAI APIs and machine learning.
The predicted salary is between 60000 - 80000 £ per year.
Are you ready to grow your data science expertise and work on impactful AI and machine learning projects? Would you enjoy building advanced analytics, machine learning, and GenAI solutions that drive real business value?
About our team: We are a fast-moving, high-impact Data Science & AI team building real-world GenAI and ML solutions across the LexisNexis business. Our work powers smarter decisions for Product, Sales, Finance, Marketing, Customer Success, and Engineering—everything from predictive models to enterprise GenAI applications to automation that transforms how teams operate. We are data science generalists who enjoy variety. One day, it may be designing a new GenAI workflow, the next it may be deploying a model into Salesforce or developing a pipeline in Databricks. We work closely with stakeholders to build practical solutions that are used and deliver measurable impact. If you want to experiment, build, ship, and see your work make a difference across a global organisation, you will feel right at home with us.
About the role: We are seeking a Data Scientist III who is a strong 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 help design, build, and deploy AI and ML solutions that support key business functions across Product, Sales, Finance, Marketing, Customer Success, and Engineering. You will contribute across the full solution lifecycle, including problem framing, data preparation, modelling, experimentation, prompt engineering, deployment, monitoring, and stakeholder communication. This position is ideal for a versatile data scientist who enjoys solving diverse problems, working across multiple systems, and contributing to measurable business impact.
Responsibilities:
- Build GenAI applications using OpenAI APIs, embeddings, vector search, and RAG.
- Apply prompt engineering and help define evaluation approaches for GenAI outputs.
- Develop and deploy ML models (e.g., churn, propensity-to-buy, sentiment/feedback, lead scoring, customer intelligence).
- Own the full ML lifecycle: data prep, experimentation, deployment, and monitoring.
- Build and optimise feature pipelines and model scoring jobs with Python, Databricks, Spark, and Delta Lake.
- Use AWS (S3, Redshift, Lambda) for data automation and orchestration.
- Improve pipeline data quality, observability, lineage, and documentation.
- Integrate models/data with enterprise platforms (Salesforce, Oracle Fusion/Service Cloud/Peoplesoft).
- Deliver real-time and batch workflows to improve CRM, sales, service, and marketing operations.
- Partner cross-functionally to define KPIs, generate actionable insights, communicate clearly, and drive adoption via demos/docs/training.
Requirements:
- Strong Python programming skills.
- 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 working with behavioural or business datasets.
- Ability to work across machine learning, analytics, data engineering, and integrations.
- Ability to contribute to end-to-end solutions spanning data, models, APIs, and automation workflows.
Data Scientist III employer: LexisNexis
Contact Detail:
LexisNexis Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist III
✨Tip Number 1
Network like a pro! Reach out to current employees at LexisNexis or in the data science field. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving GenAI and machine learning. This is your chance to demonstrate how you can drive real business value with your expertise.
✨Tip Number 3
Prepare for the interview by brushing up on your Python and SQL skills. Be ready to discuss your experience with AWS and Databricks, as well as how you've tackled diverse problems in past roles.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining our team and contributing to impactful projects.
We think you need these skills to ace Data Scientist III
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Scientist III role. Highlight your expertise in Python, machine learning, and any relevant projects you've worked on that demonstrate your ability to build impactful AI solutions.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about this role and how your background makes you a great fit. Share specific examples of your work with GenAI or machine learning that have driven real business value.
Showcase Your Problem-Solving Skills: In your application, emphasise your experience with the full ML lifecycle and how you've tackled diverse problems. We love data science generalists, so let us know how you've contributed across different systems and functions.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at LexisNexis
✨Know Your Tech Stack
Make sure you’re familiar with the tools and technologies mentioned in the job description, like Python, Databricks, and AWS. Brush up on your skills with OpenAI APIs and machine learning fundamentals, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples of how you've tackled diverse data science problems in the past. Highlight your experience with the full ML lifecycle, from data preparation to deployment, and be ready to explain your thought process and the impact of your solutions.
✨Communicate Clearly
Since this role involves working closely with stakeholders, practice explaining complex concepts in simple terms. Be prepared to discuss how you’ve communicated insights and driven adoption of your solutions in previous roles—this will demonstrate your ability to partner cross-functionally.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the team and the projects they’re working on. Inquire about their current challenges or upcoming projects related to GenAI and machine learning, which will help you understand how you can contribute effectively.