LexisNexis Legal & Professional is looking to solve key business functions across Product, Sales, Finance, Marketing, Customer Success, and Engineering by designing, building, and deploying AI and ML solutions.
Requirements
- Strong Python programming skills.
- Hands on 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, Lambda
- Familiarity with Redshift, Snowflake or other cloud data warehouses.
Responsibilities
- 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.
- Build and optimize 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.
Other
- 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.
- Strong communication and stakeholder management skills.
- Ability to operate independently with minimal direction and actively mentor junior data scientists through technical guidance and best practices.
- Bachelor's degree or higher in a quantitative field (e.g., Computer Science, Mathematics, Statistics) is implied but not explicitly stated
