About the Role
The role involves working with large datasets to identify trends, develop models, and communicate findings to stakeholders to inform business strategies and product development.
Responsibilities
- Collect and process large volumes of structured and unstructured data
- Perform exploratory data analysis to uncover patterns and anomalies
- Design and implement machine learning models for predictive analytics
- Collaborate with engineering teams to integrate models into production systems
- Evaluate model performance using statistical validation techniques
- Create data visualizations to communicate findings to non-technical audiences
- Support A/B testing frameworks and interpret experimental results
- Ensure data quality and integrity across pipelines
- Work with product teams to define key metrics and success criteria
- Develop dashboards and reporting tools for ongoing monitoring
- Apply statistical methods to solve business problems
- Document methodologies and model assumptions clearly
- Stay current with advancements in data science and machine learning
- Optimize data workflows for efficiency and scalability
- Contribute to data governance and metadata management
- Assist in defining data collection requirements
- Troubleshoot data discrepancies and resolve issues
- Support ad hoc analytical requests from stakeholders
- Participate in peer reviews of code and models
- Maintain reproducibility of analyses and experiments
- Use programming languages such as Python or R for data analysis
- Work with SQL for querying relational databases
- Leverage cloud-based data platforms and tools
- Apply version control practices to analytical workflows
- Ensure compliance with data privacy standards
Nice to Have
- Master’s degree in a quantitative discipline
- Experience with big data technologies such as Spark or Hadoop
- Knowledge of advanced machine learning techniques
- Experience deploying models into production environments
- Familiarity with containerization tools like Docker
- Experience with orchestration platforms such as Airflow
- Background in causal inference methods
- Exposure to natural language processing tasks
- Experience in insurance or financial services domain
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model
Team
Collaborative data science and engineering environment
About the Team
The data science team partners closely with product, engineering, and business units to drive innovation through data. The team values rigorous analysis, clear communication, and scalable solutions.
Growth Opportunities
This role offers exposure to end-to-end data science projects and opportunities to advance into more specialized or leadership roles based on interest and performance.
Available for qualified candidates
