What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 4+ years of applied data science experience
- Strong hands-on proficiency in Python and machine learning libraries such as Scikit-learn XGBoost LightGBM TensorFlow or PyTorch
- Good understanding of classification regression survival analysis feature engineering model tuning and explainability
- Hands-on exposure to NLP and deep learning techniques for text classification information extraction or document analytics
- Practical exposure to Generative AI and large language models including prompt engineering embeddings RAG vector databases and evaluation
- Working knowledge of MLOps and model lifecycle practices including deployment support monitoring drift detection retraining and tools such as MLflow Airflow Docker Kubernetes or cloud ML platforms
- Strong SQL and data manipulation skills with large structured and unstructured datasets
- Good understanding of relational databases data modeling and data architecture fundamentals
- Experience collaborating with cross-functional teams and communicating findings to technical and non-technical stakeholders
- Exposure to healthcare insurance disability claims or other regulated domains preferred
Nice to have
- Analytical thinking
- Structured problem solving
- Ownership
- Fast-paced adaptability
- Cross-cultural awareness
Day to day
- Design and develop machine learning and AI solutions that drive measurable business outcomes across disability insurance use cases
- Work closely with business, actuarial, and claims stakeholders to translate complex problems into practical analytics and ML solutions
- Contribute across the full analytics lifecycle including problem framing, exploratory analysis, feature engineering, model development, validation, deployment support, and ongoing monitoring