Data Scientist

About the role

We're working with a leading independent actuarial and consulting firm that develops and deploys category-defining, data-driven, software-as-a-service (SaaS) products for a broad spectrum of insurance, health IT, and life sciences clients. The role involves researching, developing, deploying, and maintaining traditional AI/ML models following industry best practices, as well as working extensively with available GenAI models to construct exciting solutions for internal and external use cases. Coordination with Product, Business Development, ML Engineering, and IT teams is required to bring new data science products to market. The role also involves driving best practices and continuous improvement on the data science team, influencing model design and experimentation strategy.

Responsibilities

• Research, develop, deploy, and maintain traditional AI/ML models
• Work extensively with available GenAI models to construct solutions for internal and external use cases
• Coordinate with Product, Business Development, ML Engineering, and IT teams to bring new data science products to market
• Drive best practices and continuous improvement on the data science team, influencing model design and experimentation strategy

Requirements

• 10+ years of professional experience using AI/ML to create high ROI commercial data science solutions
• Expertise with Electronic Health Records or unstructured data analysis
• Expert data scientist with demonstrable capability building traditional AI/ML models (Supervised Learning, Unsupervised Learning, Model Validation, Deep Learning Architectures, NLP Algorithms)
• Expert understanding of NLP and generative AI, able to effectively use, fine-tune, and evaluate commercially available models
• Hands-on experience building GenAI applications (e.g., RAG systems, LLM evaluation frameworks, or GenAI-powered internal tools)
• Expert level Python programmer, with some experience in R and/or SQL
• Expert user of Databricks or similar cloud-based model development ecosystem
• Sufficient understanding of software engineering best practices such as Git for version control, unit testing, local development, and environment management
• Knowledge of ML Engineering and ML Ops related concepts and tools including CICD pipelines, GitHub Actions, Docker, AWS Lambda, and Linux

Benefits

• Competitive compensation and benefits
• Strong entrepreneurial and collaborative culture of innovation and excellence
• Opportunities for growth in skillset, responsibilities, and career
• Investment in skills training and career development with access to learning and mentoring opportunities