Data Science Careers in Action: Putting a Master’s to Work
UNLIMITED DATA | BY JAMES KULICH | 5 MIN READ
When I recently began canvassing our graduates from Elmhurst University’s Masters in Data Science program, I expected to see good results.
What I found went well beyond what I imagined.
I fully expected that most of our alumni are making use of analytics in their work, as they are. The range and depth to which they are doing so amazed me.
What Our Data Science Graduates are Doing
Elmhurst’s MDS graduates are employed by Fortune 500 companies, by innovative startups, and by many small- to mid-size organizations. Sectors represented include health care, insurance, financial services, retail, manufacturing, logistics, media, agriculture, education, human resources, government, sports, consulting and more.
Some job titles, such as senior analyst, data scientist, and senior manager of enterprise data and analytics, directly reflect responsibilities in the data science realm.
Others, such as principal–strategic planning and analytics, supply chain advanced analytics and process improvement manager, and collateral risk innovation manager, reflect the depth to which data science is becoming strategically embedded in the fabric of organizations.
Getting Hired and Advancing
Our alumni report some common themes in the paths to their current positions. One step involves demonstrating technical skills to put data science in action. Graduates describe being asked to prepare a dataset for further analysis, using tools like Python to handle issues such as missing data and outliers. The emphasis is on getting the basics right.
This can be a difficult hurdle to clear, but once this challenge is met, the focus turns to a candidate’s ability to translate technical work into organizational value. Managers evaluate a candidate’s ability to work well in larger teams, to interface with other parts of the organization, to explain why a technical result solves the problem at hand, and to paint a pathway for achieving the promise the output of a model might offer.
I was gratified to hear repeatedly from alumni that our holistic approach gave them distinct advantages in the hiring process. Their ability to handle both technical and organizational perspectives was key to being chosen, especially for more senior positions. Graduates cited our inclusion of project management concepts in the curriculum as being especially important.
Even in this very challenging business environment, I have received several notes in recent weeks from program alumni who have landed good new jobs or who have moved into new roles.
The experiences of our alumni support the general sense that opportunities remain strong in the world of data science. But there are some caveats.
First, addressing basic short-term business needs is more important than ever. Most businesses are looking to contain costs. One graduate with whom I spoke described how his work on AI projects has been temporarily diverted toward more basic efforts to automate routine organizational tasks. This is an area known as Robotic Process Automation, or RPA.
Another basic need at this time is effective forecasting of key business metrics. Standard forecasts simply don’t work in the face of the pandemic. Organizations need new ways to get a sense of what to expect, both now and when the pandemic recedes.
Many organizations have less room than usual to experiment. They need tangible results. One graduate reports that his consulting duties have been refocused on organizational data governance, as these governance issues are perceived to be a primary roadblock to effective implementation of AI projects.
As these illustrations show, keeping the focus on producing organizational value remains essential. While this is not new, there is a now a sense of urgency.
Plenty of room remains for advanced work, such as natural language processing. Indeed, several of our alumni are active in fields such as this. But, applying data science techniques to everyday problems is the core work to be done. The path to value must now be clearer than ever.
A Larger View
The potential for data science to address some of the larger issues on our collective tables continues to grow.
One of our graduates focused her work on equitable models for public school funding. A current student is beginning work on modeling the connections between budgets and the impact of investments in government programs.
I am beginning a project with a local school district that is achieving strong results with students from lower-income families to determine the drivers behind their success and the extent to which some of their approaches could be used by others.
And, one of our earliest graduates just took a new position in the area of People Analytics with the title Diversity, Equity, and Inclusion Data Strategist.
I don’t have much more to report now on these early efforts, but I look forward to telling you more about them in future posts. I’m incredibly proud of what our Elmhurst students and alumni have achieved.
I welcome your thoughts as we look to prepare even more people to make a difference via effective use of data.