- Mexico City
- Date Posted
- Nov. 8, 2021
- Data Science
At Sofia we want to change the way millions of people take care of their health by offering a health plan that is more complete, simpler to understand, and closer to anyone who wants to live a healthier life. We want to build a thriving community that deeply changes the way the health system works.
About the role
As a Data Scientist at Sofía, you will be positioned to significantly impact the lives of millions of people and you will be continuously exposed to strategic discussions and decisions.
- Use qualitative, analytical, and predictive methods to understand how our Soci@s can improve their health while engaging with our product in a meaningful way.
- Discover, understand and structure different data sources into clean data models that unlock self-service analytics.
- Define, implement, and monitor key performance metrics for all teams across Sofía.
- Empower our operation with self-service tools (dashboards, data cubes, etc.) to better understand and serve our Soci@s.
- Work closely with Product, Engineering & Marketing/Growth teams to help them shape their roadmap and strategic priorities.
- Design experiments to evaluate initiatives that are live and identify areas for improvement.
What We’re looking for
- 3+years of work experience in a quantitative analytical role.
- Proficiency with statistical software (e.g., R, Python), database languages (e.g., SQL), and code versioning tools (e.g., Git).
- Proven experience articulating, translating, and solving business problems through data.
- Self-direction and eagerness to collaborate with other teams (Product, Engineering, Maketing/Growth).
- Effective written, verbal and visual communication skills.
- Willingness to both teach others and learn new techniques.
- Knowledge of health care, health economics, or health insurance.
- Applied experience deploying machine learning models into production.
- Experience extracting and manipulating large datasets with tools like Presto or Spark.
- Postgraduate degree in a quantitative discipline (e.g., Mathematics, Computer Science, Statistics, Economics, Actuary, Operations Research, Industrial Engineering, etc.).