As a Data Scientist Intern in the Engagement Mission, you will use data to contribute to the products that make our members happier and more productive at work. You’ll leverage data that spans across our digital and physical products to help us in creating intelligent environments and connected, consciously-engineered communities in all of our spaces.
Over the course of your summer project, you’ll work alongside our Data Scientists to extract meaningful insights and make recommendations that influence the product roadmap. You’ll contribute as part of a cross-functional product team consisting of product managers, engineers, data scientists, and designers. Ultimately you will form hypotheses and uncover new opportunities using the quantitative tools at your disposal.
The ideal person for this role is highly analytical, a creative problem-solver, passionate about generating hypotheses for business problems, excels at delivering actionable insights, and sees data science as a powerful tool to impact the business.
- Leverage your skills in problem solving, data analysis, and product sense to provide recommendations that improve our products throughout a summer-long project.
- Work alongside our Data Scientists to determine the right KPIs to measure impact for business initiatives and conduct quantitative analyses to understand the factors that are likely to contribute to those goals.
- Collaborate with Data Engineers, Machine Learning Engineers, and product teams to gain a broad understanding of the breadth of data to inform your analyses.
- Share your insights and recommendations with product stakeholders to guide the product roadmap and priorities.
- Currently pursuing a degree in a quantitative field (e.g., mathematics, computer science, physics, economics, engineering, statistics, operations research, quantitative social science, etc.).
- Ability to break down and understand complex business problems, define a solution and implement it using advanced quantitative methods.
- Familiarity with programming for data analysis; ideally Python, SQL, or R.
- Technical understanding of statistical analysis, machine learning (classification, regression, unsupervised, reinforcement, etc.), predictive modeling, and optimization algorithms.
- Solid oral and written communication skills, especially around analytical concepts and methods.
- Great work ethic and intellectual curiosity.