Role Overview :

We are seeking two skilled Data Scientists to join our engineering team to develop and implement a high-impact AI/ML predictive modeling solution.

In this role, you will analyze historical claims data to generate VIN-level risk scoring and loss ratio forecasts for the Extended Service Business (ESB) Core Segment.

Your work will directly influence product re-pricing and claim adjudication strategies, with a targeted objective of $1 million in annual claim cost reductions.

Key Responsibilities :

1. Model Development :

- Design, build, and deploy end-to-end machine learning models on Google Cloud Platform (GCP) to predict loss ratios and identify high-utilization contracts at the VIN level (Requires 3-5 years of experience).

2. Data Engineering & Feature Creation :

- Ingest and transform high-volume historical claims data from OWS and enterprise source systems; engineer robust features that capture granular risk factors.

3. Advanced Analytics :

- Apply statistical modeling and ML algorithms (regression, gradient boosting, etc.) to drive proactive risk assessment and dynamic product pricing.

4. Stakeholder Collaboration :

- Partner with business analysts and product teams to translate model outputs into actionable financial forecasting and strategic claim adjudication workflows.

5. Operationalization :

- Ensure model scalability and performance monitoring, contributing to a framework designed for future expansion across global markets.

Required Technical Skills :

1. Cloud Proficiency :

- Hands-on experience with Google Cloud Platform (GCP) specifically BigQuery, Vertex AI, and Cloud Composer.

2. Core Data Science :

- Strong proficiency in Python/PySpark for data manipulation and model building (Scikit-Learn, XGBoost, LightGBM, or TensorFlow).

3. Database Expertise :

- Advanced SQL skills for complex data retrieval, aggregation, and performance optimization within large datasets.

4. Model Lifecycle :

- Demonstrated experience in the full ML lifecycle : data cleaning, feature engineering, model training, validation, and deployment (MLOps).