Role Description
We are looking for an experienced Data Scientist with experience in Insurance Pricing, with the aim of transforming complex data into actionable insights that drive our insurance operations. In this role, you will develop advanced analytics models to guide strategic decisions and solve challenging business problems, partnering closely with cross-functional teams and our Pricing experts.
Main Responsibilities
- Improvement of existing models to offer our clients segmented pricing that enables profitable growth through pricing optimization.
- Optimizing insurance renewal pricing to balance profitability, market positioning, and quality standards.
- Develop and deploy advanced statistical and machine learning models to address complex challenges, including pricing optimization, claims forecasting, risk assessment, and customer lifetime value (CLV).
- Partner with domain experts to translate ambiguous business questions into data science frameworks.
- Partner with the MLOps team to ensure seamless model deployment, monitor for data and concept drift, and adhere to CI/CD and automated retraining best practices.
- Design and analyze rigorous A/B and multivariate experiments to validate the efficacy of new algorithms and business strategies.
- Communicate findings and insights to both technical and non-technical stakeholders.
- Collaborate with cross-functional teams, including software engineers, product managers, and business analysts, to integrate your models into production systems.
- Stay up-to-date with the latest advancements in data science and technology.
Requirements:
This is you!
- Minimum of 3 years of experience in advanced analytics, with a proven track record of solving complex optimization or predictive modeling problems in production.
- Proven experience in a Pricing area of an Insurance company
- Expert-level proficiency in Python (e.g., Pandas, Scikit-Learn, PyTorch/TensorFlow) and advanced SQL.
- Proven experience in predictive analytics and price optimization platforms (Radar, Emblem, Akur8, Earnix)
- Deep understanding of applied statistics and optimization algorithms.
- Experience building rating areas, vehicle scores, indemnity cost models and/or demand models.
- Nice to have: Experience navigating big data environments/cloud platforms (AWS, GCP, Azure) and hands-on familiarity with model deployment.