Culture and Talent

A day in the life of Clarity Money’s chief data scientist

  • Chief scientists are becoming increasingly strategic at fintech firms.
  • We followed co-founder of Clarity Money Hossein Azari around to see just how data drives his firm's technology.

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As fintech algorithms and big data solutions become increasingly advanced, the role of chief data scientist has turned more strategic. That’s because the role contributes to product, marketing, and sales teams. It’s even more core when the chief data scientist is a co-founder like Clarity Money’s Hossein Azari.

We followed Azari around to see what he works on in a typical day working at the personal finance management firm he co-founded with Michael Dell’s brother.

Hossein Azari at Clarity Money

7 a.m.: Wake up, have some orange juice and spend some time gathering my thoughts and planning for the day. Really, I’m already working, using the time to put all technical issues to the side, and remember some of my big picture goals. These can include going over first principles like putting the user first or creating consistencies in data platforms and interpreting how these impact our current tasks.

8 a.m.: Have breakfast, usually eggs, and check school-related emails from the executive MBA at Columbia Business School I attend on Saturdays… so every second of time is precious!

8:30 a.m.: I read while on the subway (the 6 train) — mostly fintech news and summaries from different sources. The fintech industry has such a large group of entrepreneurs and investors that being up to date with the direction and pivots of the industry is crucial for making decisions, like what we should focus on building. Or, should we build a system that’s objectively superior or should we build it only to be better than our competition and move on to another system?

9 a.m.: Sit down at my desk at Clarity, log into my computer and immediately start reviewing metrics and KPIs for each of our systems. I then go over emails and messages. I monitor the performance of different algorithms and models that we have built and discover bugs, inefficiencies or decide to improve parts of the system based on day to day monitoring.

10 a.m.: Sync with data engineers to discuss questions and issues about new tasks and to set the methodology requirements for new additions to our product. We also take the time to talk about an issue with our current algorithms, formalizing a solution. Our goal is to make sure the user has the best, most optimal experience.

10:30 a.m.: Execute ideas we have on new algorithms and models. Frequently, I’ll deep dive into our data to shape new metrics, building intuition about our users in a search to find patterns. We build insights based on users’ financial data and provide these insights in an actionable way to the user. This task uses statistical methods to accurately estimate and detect parameters such as income, outliers and spending patterns. We also employ high-level intelligent systems that react to users’ interactions with the insights presented by the Clarity app and present them in the most efficient way for each user.

Noon: It’s time for lunch, sometimes with a friend or catching up with a former colleague in town. We order in or walk out for lunch with the team. I set up my lunch meetings in Burger and Barrel (my favorite) right next to the, but my colleagues often talk me into to Chopt for a salad.

1 p.m.: As co-founder and chief data scientist, I need to wear different hats. Depending on the day, I attend different types of meetings that include investor meetings, interviews, and just brainstorming with the team. I have clear goals for our data science efforts and I make sure to communicate well with stakeholders and receive feedback. I also spend some time to think about larger problems that need alignment with our data science approach.

2 p.m.: Some more programming and model building or writing up, documenting ,and logistics planning. This is mostly my solo time when I am in front of my double screen, writing and running things. I enjoy this part a lot, especially after a full day of  meetings, chatting, and lunchtime burgers.

5 p.m.: The team gets back together once again, and we solve some problems together. This mainly involves following up on the day’s tasks that are being implemented and understand where we stand for tomorrow.

6 p.m.: Leave office…on a good day. It really depends on the tasks, deadlines, and problems that need to be solved that day.

6:30 p.m.: Either attend an after work dinner with a friend or former colleague from Google or Harvard or current colleagues from Columbia. Sometimes I attend a meetup and meet new people. I try to attend major meetups and conferences and make sure to have an exposure to key people in the fintech industry. Reading news on fintech companies is helpful but by talking to people from these companies, I can learn much more about the fintech scene.

If I don’t have any plans, I go home and have dinner with my wife, Elham, spending some time discussing our days. She is a research fellow at Memorial Sloan Kettering Cancer Center and uses machine learning and data science to tackle problems in cancer research. It is very inspiring and refreshing to hear about how data science is being used to tackle one of the biggest health challenges of our time.

8 p.m.: Work on some of my school projects or my reading list. I like to read from people who have experienced and done great things, especially in the tech sector, and live through their challenges while reading about them. Andy Grove’s biography has inspired me a lot. The next book on my list is Shoe Dog by Nike founder, Phil Knight.

11 p.m.: Go to bed, rest from the day, and get ready to conquer the new challenges I’ll face tomorrow.

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