At The Data Incubator we run a free eight-week Data Science Fellowship Program to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring Data Scientists. Jeiran was a Fellow in our winter cohort who landed a job with one of our hiring partners, Chartbeat.
What are you doing now and how did you get there?
At NYU, I studied the brain and its blood circulatory network through MRI data. I worked on spatio-temporal noise reduction techniques and developed models to detect and quantify different patterns of blood flow in different regions of the brain. During my graduate years, I learned to convey results visually and build concise narratives. Every sophisticated statistical tool or machine learning algorithm stems from a simple intuitive understanding of the problem, which can be used to construct your narrative. I use this skill every day at Chartbeat.
A rough answer to the right question is preferable to an exact answer to the wrong question. In other words, statistics is only a tool that can be used to answer questions, but it cannot always tell us what the question is. The imperative is to ask the right questions, which are informed by your data. I learned that data comes first, and inference and modeling follow. I also learned that every estimate should come equipped with a measure of uncertainty. I learned this by working with real-world data. My research had the right balance of theory and application for my current role at Chartbeat.
What do you think you got out of The Data Incubator?
Last but not the least, I established a great network — composed of not just the Incubator Fellows in my cohort but also previous Fellows who have been data scientists at top firms and startups. We also met many chief data scientists at different companies. They came to the Incubator for panel discussions and happy hours on a regular basis, and we met them up-close and asked them all we wanted to ask. This network facilitated the job hunt and turned it into a much more pleasant experience.
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
Could you tell us about your Data Incubator Capstone project?
These documents provide a very high spatial and temporal resolution view of the conflict. For example, I extracted from these government memos the number of violent events per day in each county. Then, using latent factor analysis techniques, e.g. non-negative matrix factorization, I was able to cluster the top three principal war zones. Interestingly these principal conflict zones were areas populated by the three main ethnoreligious groups in Iraq. Moreover, adaptive Bayesian smoothing approaches revealed statistically significant jumps in the underlying temporal trends within each cluster — the so-called spike alert days. These spike alert days coincided with well-documented changes in the way the war was handled. Although the algorithms used to analyze the war memos were blind to the historical, geographical, and political context of the conflict, they were able to shed light on decisions that exacerbated it.
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- Data Science Fellowship – a free, full-time, eight-week bootcamp program for PhD and master’s graduates looking to get hired as professional Data Scientists in New York City, Washington DC, San Francisco, and Boston.
- Hiring Data Scientists
- Corporate data science training
- Online data science courses: introductory part-time bootcamps – taught by our expert Data Scientists in residence, and based on our Fellowship curriculum – for busy professionals to boost their data science skills in their spare time.