Kehinde Rilwan Salau

What He Does

Kehinde works as a data scientist within marketing decision sciences to support the mortgage banking division of JPMC. He collaborates with fellow team members to help the firm make informed decisions about mortgage production by providing quantitative support in the form of volume sizing and forecasting, customer propensity models, and developing test designs for the efficacy of marketing tactics.

Pros and Cons Of His Job

One wonderful thing about JPMC is the plethora of departments needing advanced analytics support (e.g. mortgage banking, asset management, business banking, consumer banking, fraud, etc.) and the ability to work across those departments. Professionals, can grow their technical expertise while learning to communicate results to stakeholders across a variety of internal business units.

The most important thing, i found, is being in an environment that’s right for you in terms of performing tasks that allow for personal enjoyment and professional growth.

Career Path

Kehinde was on the verge of graduating with his Bachelor’s degree in 2007 but had no plans for the next phase of his life. Two summer internships with the Mathematical Theoretical Biology Institute convinced him that the next stage of his learning lay in the field of applied mathematics. The internships led to an opportunity to continue his development as a funded research and teaching applied math graduate student.

Six years later Kehinde is a Ph.D. in applied mathematics focusing on topics in mathematical ecology, optimal control, natural resource economics, and network theory. He took a postdoc position that allowed him to further his studies and build a bigger network of collaboration.

Though enjoyable, a lot of Kehinde’s work was theoretical and he started to yearn for some practical experience tackling more immediate real-world issues with quantitative, data-driven techniques. So he began tilting his studies towards statistics and data science. He taught Introductory Statistics courses as a postdoc, enrolled in online data science courses, and learned to use new statistical programming tools.

Numerous entities in the academic, industrial, and government sector are working to make sense of growing pools of data and leverage this information for insight and decision-making. For a data scientist, gainful research can be found in any one of these sectors or at their intersection. Kehinde wanted hands-on experience (1) with the kinds of products people interact with every day and (2) collaborating with individuals of diverse background developing statistical models with actionable output. This desire led him to the financial services industry and to his current position as a member of a marketing decision sciences team.

Career Expectations and Advice

The joy of applied math is it allows practitioners to be immediately effective as quantitative contributors, and then opens doors to allow individuals to deepen their knowledge of a chosen applied field (finance, biology, policymaking, you name it). Don’t be bashful about walking through as many doors as you want in any field you desire, don’t be bashful about switching careers between the three sectors and/or choosing careers at the intersection. When searching for a career that will keep you happy and engaged for 2–30 years it’s acceptable and encouraged to cast a wide net and explore the contents. Finally, it is at times helpful to chart 5-, 10-, or 15-year goals, but that may crumble if you don’t enjoy the day-to-day processes necessary for attaining those timed goals. So enjoy yourself as best you can as you grow.

Divorce yourself from the thinking that there are distinct boundaries between academia, industry, and government.

Salary

Between $85,000 and $120,000 depending on academic degree and years of experience (postdoctoral appointments may count as post academic experience).