What is it and who’s crunching those numbers?
Looking for trends in large swaths of data is a simple enough concept but is certainly difficult in execution.
Tell us a little about yourself and your experience.
Today, I am the Director of Data Science forThink Bigand have been with the company for four years.
What drove you to choose your career path?
Working to help businesses become data driven seems to be a professional extension of that identity.
How did you go about getting your job?
What kind of education and experience did you need?
Professionally, I moved through different roles doing statistics, research, and technology early in my career.
From there, I got into big data in 2010.
But it took me a year or so to really appreciate whatHadoopand similar tools could do for data science.
What kinds of things do you do beyond what normal people see?
What do you actually spend the majority of your time doing?
What misconceptions do people often have about your job?
The single biggest misconception in data science is that it’s all about “algorithms.”
In truth, data science begins by translating a business case into an analytics agenda.
What are your average work hours?
Data scientists are professionals and should expect a professional work week.
Nowadays, that seems to be 60 hours per week.
What personal tips and shortcuts have made your job easier?
It also supports other scientists looking at the same data in later months.
A second tip is to make a “runbook” after doing any modeling.
This ensures our work is repeatable, even by yourself.
It’s easy to forget an analysis from three months ago when you are busy.
What do you do differently from your coworkers or peers in the same profession?
What do they do instead?
I spend less time chasing new technologies than many of my peers.
Instead, I focus on a core set with which I am familiar.
Today, tools likeHiveover Hadoop,R, andPythonget me very far.
What’s the worst part of the job and how do you deal with it?
To deal with this, we have checklists we cover before starting a project.
What’s the most enjoyable part of the job?
I love seeing customers become data driven.
That’s the real goal of data science and it’s beautiful to see it in action.
Do you have any advice for people who need to enlist your services?
And I have seen these groups dissolve in their lack of mission.
Similarly, don’t land a data scientist and expect them to build you a business.
Your job role most likely looked for statistics and technology skills.
Have a goal and a plan to join those skills with your business drivers before you even start hiring.
What kind of money can one expect to make at your job?
It certainly varies but is a well-paid role.
Even first year data scientists often make over $80k.
Seasoned data scientist salaries vary by where they sit in the organization.
Those in technical roles leading teams can certainly make more than double that.
Those can make up to $400k.
How do you move up in your field?
There are multiple paths.
What do your clients under/over value?
They undervalue the importance of clearly defined and communicated KPI.
In enterprises, the relationship between throughput and revenue is complex and slow to evaluate.
What advice would you give to those aspiring to join your profession?
Spend as much time learning analyticscommunicationas learning models.
If you’d like to share your career, email us at[email protected].
Image adapted fromNemo(Pixabay).
