Senior Data Scientist II

Senior Data Scientist II

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 Lexis Nexis 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.

Key responsibilities AI, GenAI and Machine Learning

• Build GenAI applications using OpenAI APIs, embeddings, vector search, and retrieval augmented generation.
• Develop advanced prompt engineering patterns and automated evaluation frameworks.
• Build and deploy traditional ML models, including churn prediction, propensity to buy, customer sentiment and feedback analysis, lead scoring and customer intelligence models, etc.
• Own the full model lifecycle, including data preparation, experimentation, deployment, and monitoring.

Data Engineering and Cloud Work

• Build and optimise feature pipelines and model scoring jobs using AWS, Python, Databricks, Spark, and Delta Lake.
• Leverage AWS services, including S3, Redshift, and Lambda for data automation and orchestration.
• Ensure data quality, observability, lineage, and documentation across pipelines.

Enterprise System Integrations

• Build and deploy model and data integrations with:
  Salesforce (SFDC), Oracle Fusion, Oracle Service Cloud, Oracle Peoplesoft
• Support real‑time and batch workflows that enhance CRM, sales, customer service, and marketing operations.

Analytics and Insights

• Collaborate with cross‑functional teams to define KPIs and develop analytics solutions.
• Provide insights that connect customer behaviour, product usage, finance, and CRM data.
• Translate insights into actionable recommendations that support product, sales, and customer strategy.

Productionisation, Reliability and Support

• Provide L2 and L3 support for AI and ML pipelines.
• Implement monitoring for model drift, data quality, and prompt performance.
• Lead root‑cause analysis and build preventive systems for long‑term stability and reliability.

Cross‑Functional Collaboration

• Partner closely with Product, Engineering, Finance, Sales, Operations, Marketing, and Customer Facing teams.
• Translate business challenges into AI and ML solutions with clear ROI.
• Communicate technical concepts in a clear and actionable manner for non‑technical stakeholders.
• Support the adoption of AI and ML solutions through demos, documentation, and training.

Requirements Core Technical Skills

• 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…