Meet an Early Career Ecologist: Bijan Seyednasrollah
For this month’s blog post, meet Dr. Bijan Seyednasrollah, the Lead Data Scientist at Realtor.com (News Corp)!
Tell us about yourself!
In short, I am a multidisciplinary data scientist as I have occasionally switched fields throughout my career. After getting a bachelor and a master in mechanical engineering, I worked as an engineer in the petroleum industry for about 6 years in Iran. Then I came to the US and got my PhD from Duke University where I studied how the carbon, water and energy cycles are affected by climate change across North America. I then joined Harvard University as a postdoctoral scientist to observe and quantify global environmental change using digital cameras. Right now I am a data scientist in the real estate industry. I’m married to a super supportive woman who is also a geoscientist and we have a super cute two year old daughter.
Describe your current job and background
In my current job, as a Lead Data Scientist in my team, I build models to help understand and forecast various features of the real estate market in the US. Because of the high volatility of the market, as well as its temporal and spatial dynamics, the real estate industry has become a very attractive domain for data science with lots of opportunity.
Share something non-work related that matters to you or you’re passionate about.
I am passionate about learning new skills, creating new things, and solving difficult applied problems, especially when they have large impacts. I also love how events, entities and people are connected over time and across the geography. I enjoy reading about history, and macroeconomics.
Why did you decide to look for a non-ecological (or non-academic) career? Was there a key turning point that precipitated this career change?
This is a great question and many of my friends have reached out to me asking the same question, they are curious about “why” and then “how”. Many of them are thinking and planning about making the same decision.
When I was in academia, I had a productive portfolio as an academic, in particular, as this is often measured by the number of publications. In fact, I had my most productive period towards the end of my time at academia when I published seven lead-author papers only in the last two years. However, I was not super happy. I blame most of that on the academic culture and expectations.
On one hand, early career academics are expected to be extremely productive in many aspects and their job is in fact several jobs compressed in one: leading “super cool” research, publishing high impact papers, presenting at international meetings, creating strong teaching portfolio, mentoring junior scientists and students, being great in community outreach, writing grants and raising research funding, and on the top of that compete with many other great scientists on very few tenure-track jobs.
On the other hand, they are usually paid very low, especially when compared to the time they have spent in the field. Another problem with academia and particularly for early career scientists is that academia is not a “meritocracy”. This statement might sound controversial to some and they may disagree with that but when you are an early career scientist or when you listen to other early career scientists, it is hard to unsee what you see on what fraction of decisions in academia are made mostly based on political reasoning.
I think overall these conditions put substantial mental, psychological and economical burden on early career scientists which should not be overlooked and it is certainly unhealthy so I decided to opt out of this game.
What does a typical work day look like for you?
In our team, we usually work on two-week “sprints” which we commit accomplishing a task with certain details by the end of the sprint. An end-to-end data science project may span over several sprints in which many data scientists and engineers work together. So on a daily basis, it slightly varies but overall it may include understanding the problem, identifying the appropriate datasets or perhaps creating one, designing a proper data science solution, implementing the solution, overseeing junior team members, collaborating with peers, presenting the work to the technical and non-technical stakeholders, giving updates to the manager and planning for the next pieces of the work.
What projects (research or not) are you most excited about now?
I am excited about large scale problems where the outcome of the work has an impact on many people and they can benefit from that. I also love when I find an opportunity to automate a manual process, which usually means higher speed and higher accuracy.
What experiences/skills from your ecological career have been most useful in your data science career?
Certainly many skills from my ecological life were helpful and I like to divide them into two main categories:
- Soft skills: Such as experiment design, team work, leadership, communication, peer-review, problem solving, critical thinking, mentorship and sharing knowledge.
- Quantitative skills: Such as working with extensive datasets, quantitative and statistical methods, hypothesis testing, geospatial analysis, time-series analysis and programing.
What is the most fulfilling part of your job?
The US real estate market size was estimated over 10 trillion dollars last year which indicates a very high impact on many americans. As a data scientist in this industry, creating something novel that has impacts on many people across the country who are going to possibly make their largest financial transaction in their life-time is the most fulfilling part of my job.
What is one challenge you’ve dealt with, and what success are you most proud of?
I think one challenge from leaving academia and joining the industry is to rebrand yourself under terminologies that are more familiar to the industry. Many of the academic skills are marketable but for a smooth transition into industry those skills need to be rebranded to the vocabulary that is more common in industry. Another challenge is when you one to create a single or two-page high-level resume from a formerly multiple page detailed CV, which technically means “painfully” dropping a large part of your professional portfolio from your CV.
Depending on the job that you would be applying for, listing all of your “beautiful” publications or conference presentations or the courses you taught might lower your chance of getting an interview, so while creating your resume you would keep asking “should I keep this?”, “should I drop it?”. I had to go through the same dilemma and I am certainly proud of going from a 11-page CV to a 1.5 page resume.
What advice would you give to other early career ecologists interested in non-academic or data science careers?
My advice to other early career ecologists is this: Clear your thoughts and make up your mind now. If you absolutely love what you are doing in academia, maybe it is good for you, but do not be afraid of leaving academia just because you have spent a lot of time in it. Even if your profession does not perfectly align with your academic degree, you can still be passionate about ecology and enjoy what you know about the environment in everyday life or even as a hubby.
Any other thoughts you would like to touch on?
My word to all early career scientists: Follow your passion, follow your passion, follow your passion!
As my last word,
I like to thank you very much for your passion in representing early career scientists in ESA and the efforts you put in for all of us. And I greatly appreciate the interview opportunity that you gave me. Thank you.