As part of the Meet the Courserians blog series, we caught up with Emily Glassberg Sands, Director of Data Science at Coursera. Emily co-founded and is an active leader within Coursera’s Women Employee Resource Group, and contributed to the Coursera blog on the topics of female role models in STEM and diversity in the data science field.
What was your path to Coursera?
I joined Coursera three and a half years ago out of an Econ PhD at Harvard. In grad school, my work focused better understanding labor markets and consumer decision-making. Part of my research quantified why and how job referrals matter in getting a position and in productivity on-the-job. I’ve always cared about driving more equality of opportunity in the labor market. Unlocking access to career-relevant learning and credentials is one powerful lever, and I see no company better positioned than Coursera to fulfill that mission.
What is challenging about your job here?
Coursera has tremendous reach, a deep catalog, strong technology assets, an incredible team. To some extent the opportunities are endless. One of the biggest challenges is prioritizing. This includes deeply understanding our learner segments and their core needs, and then delivering a platform that meets those needs. Data plays a key role in both. I see a future where we’re serving a wide range of high-impact learning experiences and credentials to learners from every educational background in every domain. But getting there first demands focused solutions for our core segments.
What is fulfilling about working at Coursera?
A lot of things. The endless opportunity to uncover real learner challenges and solutions through observational data and experimentation — like when and why learners are dropping out of courses and which interventions can we deliver to help them persist. Watching those solutions take shape and have impact across millions of learners. And most of all, seeing measurable growth in the outcomes learners are experiencing as a result of their time on Coursera. We track where and how we’re helping learners meet their goals. This includes data on the skills they’re learning and how deeply they’re learning those skills. It also includes data on the new jobs and promotions our learners are securing as a result. The raw scale and growth of outcomes for learners is super fulfilling.
What’s something you’re passionate about?
I’m passionate about equalizing access to rewarding careers. There are a number of different approaches and no single silver bullet. One approach that’s always been core to Coursera is increasing access to learning opportunities. In the knowledge economy, skill development is crucial. Another that we’re starting to lean in on more is finding ways for people to credibly signal their skills to employers. It’s about making sure an individual is rewarded for what she actually knows.
What would you say to someone considering a data science position at Coursera?
There are two main reasons to come to Coursera. The first is you care deeply about the mission of providing life-transforming learning and credentials at scale. The second is that you find the data and the opportunity created by that data fascinating. Data science means a lot of different things at different places. One thing that is certainly true about data science at Coursera is that a huge part of the job is analytical creativity. The challenges we’re tackling are new and hard. How can we balance flexibility with structure in online learning? How can we automate paths a learner reach her goals? As a team, we come at these challenges from different angles. This includes a solid blend of inference and prediction. It’s incredibly stimulating, and a great place to grow. I’ve been here three and a half years and I can’t imagine having learned this much in that timespan anywhere else.
Interested in becoming part of the Coursera team? Check out our Careers Page to learn more about available positions, company values, and the perks and benefits of working at Coursera.12