Senior Data Scientist

Job Summary

The key area of responsibility in the role of Senior Data Scientist is to work in conjunction with our business development and product teams to develop and implement commercially viable model-based solutions to the healthcare, insurance, life sciences and adjacent markets.

• Research, develop, deploy, and maintain traditional AI/ML models following industry best practices
• Work extensively with available GenAI models; construct exciting solutions to internal and external use cases across markets and enhance our internal capabilities through centralized internal tooling and thought leadership
• Coordinate with Product, Business Development, ML Engineering, and IT to bring new and exciting data science products to market, as well as support existing industry leading products
• Help drive best practices and continuous improvement on the data science team; influencing model design and experimentation strategy through planning, audit, peer review, and other coaching

Responsibilities

• Excellent communication skills, in person and through phone / email
• Proven ability to understand client analytical needs, translate them into an action plan, then execute and deliver
• Customer-centric approach to finding solutions
• Focused on results and able to explain findings in a way that answers business problems
• Constructive, “can do” approach to overcoming obstacles
• Can quickly learn new techniques and technologies
• Proactive in identifying process improvements
• Strong work ethic, willing to pitch in wherever needed
• Thrive in a small team, without micromanagement
• Ability to manage projects and timelines, including directing the work of others

Qualifications

• 10 years of professional experience using AI/ML to create high return on investment 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: Linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM, CatBoost), and ensemble methods
• Unsupervised Learning: K-means clustering, hierarchical clustering, PCA, and anomaly detection algorithms
• Model Validation: Cross-validation strategies, hyperparameter optimization (Grid Search, Random Search, Bayesian optimization), and A/B testing frameworks
• Deep Learning Architectures: Neural networks, transformers, and transfer learning methodologies
• NLP Algorithms: Text preprocessing, TF-IDF, word embeddings (Word2Vec, GloVe), topic modeling (LDA), sentiment analysis, and named entity recognition
• Expert understanding of NLP and generative AI; able to effectively use, fine-tune, and evaluate commercially available models as well as deploy and integrate local LLMs into the data science process
• 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 including mlflow, experimentation organization, data catalogs, and compute cluster configuration
• 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
• Degree in a relevant field (computer science, data science, statistics, mathematics, applied math, actuarial science, economics, etc.)