At a Glance
- Tasks: Lead impactful data science projects and build advanced machine learning models.
- Company: Join a fast-moving Data Science & AI team at LexisNexis.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with diverse challenges and excellent career advancement potential.
- Why this job: Make a real impact with cutting-edge GenAI solutions across a global organisation.
- Qualifications: Strong Python skills and experience with OpenAI APIs and machine learning fundamentals.
The predicted salary is between 60000 - 80000 € per year.
Are you ready to take your data science expertise to the next level and lead impactful projects? Would you enjoy working on advanced machine learning models and cutting-edge analytics solutions?
About Our Team
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. 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.
- Ability to design end-to-end solutions spanning data, models, APIs, and automation workflows.
Senior Data Scientist II in London employer: LexisNexis
At LexisNexis, we pride ourselves on being an exceptional employer that fosters a dynamic and innovative work culture. Our Data Science & AI team thrives on collaboration and creativity, offering employees the chance to lead impactful projects while utilising cutting-edge technologies in a supportive environment. With ample opportunities for professional growth and the ability to see your work drive real change across a global organisation, joining us as a Senior Data Scientist II means becoming part of a team that values your contributions and encourages experimentation.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist II in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science field and let them know you're on the lookout for opportunities. Attend meetups, webinars, or conferences related to AI and machine learning to meet potential employers and learn about job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving GenAI and machine learning. Share your work on platforms like GitHub or even your own website, so hiring managers can see your expertise in action.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common data science interview questions and be ready to discuss your past projects in detail. Remember, they want to see how you think and approach challenges!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight your experience with Python, AWS, and machine learning, and show us how you can make an impact at StudySmarter.
We think you need these skills to ace Senior Data Scientist II in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your Python programming skills and experience with OpenAI APIs in your application. We want to see how you can bring your data science expertise to the table, so don’t hold back!
Tailor Your Application:Take a moment to customise your application for the Senior Data Scientist II role. Mention specific projects or experiences that align with our focus on GenAI and machine learning solutions. This helps us see how you fit into our team.
Be Clear and Concise:When writing your application, keep it clear and to the point. We appreciate straightforward communication, so make sure your key achievements and experiences shine through without unnecessary fluff.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to make an impact with us!
How to prepare for a job interview at LexisNexis
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, Databricks, and AWS. Brush up on your experience with OpenAI APIs and LLM workflows, as these will likely come up during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific projects where you've tackled diverse data science challenges. Be ready to explain your thought process, the methods you used, and the impact your solutions had on the business. Real-world examples will make you stand out!
✨Understand the Business Impact
Since this role involves driving measurable business impact, think about how your work has influenced previous projects. Be prepared to discuss KPIs and how your data-driven decisions have led to smarter outcomes for stakeholders.
✨Engage with Stakeholders
This position requires collaboration with various teams. Practice articulating how you’ve effectively communicated with non-technical stakeholders in the past. Highlight your ability to translate complex data insights into actionable recommendations that everyone can understand.
