Requirements

Expected technologies

Python

R

SQL

Our requirements

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
• Experience with statistical data analysis, statistical modelling and experimental design.

Optional

• 8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
• 4 years of experience as a data scientist, and experience applying advanced analytics to planning and infrastructure problems.
• Experience with Large Scale Computing and Predictive Modeling in a business context.
• Experience with Mathematical or Combinatorial Optimization.
• Experience designing and building machine learning models.
• Excellent problem-solving and project management skills.

Your responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into analysis, evaluation metrics, or mathematical models.
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
• Design and evaluate models to mathematically express and solve problems with limited precedent. Use statistical approaches to quantify uncertainty in data and in results.
• Use mathematical optimization/operational research methods to improve products' efficiency and customer experience.