Growing Data Science Talent Through Structured Problem Solving
Growing Data Science Talent Through Structured Problem Solving


What makes a data science team more than a collection of smart individuals feeding data through algorithms? At Macy's, the core skills that unite the data science team are a knack for structured problem solving—taking a vague business task, breaking it down to understand the underlying question, and then abstracting the question to components solvable with math—and structured communication, which allows us to effect change in our organization. In order to exercise these skills, it is also important that everyone on our team has a specific objective aligned to the global team charter, in order to set expectations about what we can and should deliver for our business. Managing our data science team therefore means supporting each data scientist as they take end-to-end ownership of mission-oriented challenges, using a combination of problem-solving, machine learning, and communication skills.

During this talk we will focus on methods to approach business problems, transforming an unorganized or broad question (e.g. should we make decision x? or, how can we increase the profitability of y?) into a set of solvable steps that can be informed by data. Through some hands-on activities and example business cases, we hope to show that having a data-driven team means that we not only use our data to devise strategies to help our business, but also that we can clearly communicate those strategies to influence decisions at all levels of our organization. As data scientists we know we can do more than plug-and-play with scikit-learn, and we hope this talk will help both data scientists and their managers take more ownership of their role and use a structured approach to their problem solving and communication.


Jolene is a senior data scientist working within the Macy’s Supply Chain organization, leading projects related to improving the return on fashion inventory through enhanced pricing and allocation strategies. She is passionate about developing intuitive explanations for how models work, and influencing the organization to adopt analytics into business practices. Before Macy’s, she studied the vibrational modes of fluorescent nanoparticles using lasers and liquid helium at MIT.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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