At a Glance
- Tasks: Lead impactful data science projects using advanced machine learning and analytics solutions.
- Company: Join a fast-moving, high-impact Data Science & AI team at LexisNexis.
- Benefits: Enjoy country-specific benefits, competitive salary, and a supportive work environment.
- Other info: Dynamic role with opportunities for growth and collaboration across various business functions.
- Why this job: Make a real impact with your data science skills across a global organisation.
- Qualifications: Strong Python skills, experience with OpenAI APIs, and solid machine learning fundamentals required.
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 employer: LexisNexis
At LexisNexis Risk Solutions, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. Our commitment to employee growth is evident through our comprehensive benefits package and opportunities for professional development, ensuring that you can thrive in your role as Vice President of Strategy, Data Services. Located in a dynamic environment, we empower our team to make impactful decisions that shape the future of data services while enjoying a supportive work atmosphere that values well-being and work-life balance.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist II
✨Tip Number 1
Network like a pro! Reach out to current employees at LexisNexis on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Senior Data Scientist II role. Personal connections can make a huge difference!
✨Tip Number 2
Showcase your projects! Prepare a portfolio that highlights your work with GenAI, machine learning models, and analytics solutions. When you get the chance to chat with recruiters or during interviews, share specific examples of how your work has made an impact.
✨Tip Number 3
Practice makes perfect! Brush up on your Python skills and be ready to discuss your experience with OpenAI APIs and AWS services. You might even want to do some mock interviews with friends or use online platforms to get comfortable with technical questions.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team. Don’t forget to tailor your application to highlight your versatility as a data scientist!
We think you need these skills to ace Senior Data Scientist II
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Senior Data Scientist II role. Highlight your expertise in Python, machine learning, and any relevant projects you've led that showcase your ability to drive impact.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're excited about this position and how your background makes you a perfect fit. Share specific examples of your work with GenAI or traditional ML models that demonstrate your versatility.
Showcase Your Projects:If you've worked on interesting data science projects, don’t hesitate to include them! Whether it's building AI applications or deploying models, we want to see how you've tackled challenges and delivered results.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you get the attention you deserve!
How to prepare for a job interview at LexisNexis
✨Know Your Tech Stack
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. Highlight how you’ve designed, built, and deployed solutions that made a measurable impact, especially in areas like predictive modelling or automation.
✨Understand the Business Impact
Familiarise yourself with how data science drives decisions across different business functions. Be ready to explain how your work can support Product, Sales, Finance, and other teams, and think of examples where your insights led to actionable recommendations.
✨Engage with Stakeholders
Since this role involves direct collaboration with stakeholders, practice articulating how you would communicate complex data findings to non-technical audiences. Show that you can bridge the gap between technical solutions and business needs.
