Q&A with Wesley Engers, Data Science, Math and Statistical Consultant
It’s easy to get drawn into the misapprehension that all good analytics work is being done by in-house teams at large companies like Google and Facebook.
There is a wide world of opportunities out there for freelancers and consultants to take their data skills out on the road and bring value to a wide range of clients. Which is what today’s guest, Wesley Engers, has been doing.
Wesley is a long time top-rated data science consultant on Upwork, a teacher and tutor on maths and stats and now runs his own analytics consultancy, Mentor Analytics.
I find out more about Wesley’s journey along this career path and his tips for success for those interested in following a freelance data career.
Welcome to Analysts Assemble, Wesley Engers.
Tell us a bit about yourself, how did you get into the data space and what does your data journey look like so far?
I’ve always been mathematically and analytically minded. I’m very curious about the world and finding ways to systematically analyze and understand it.
I studied math in college and for my graduate studies but my focus was always on applied math. Hence my studies also focused on finance and physics and learning the math was really a key step in helping understand the world around me.
After graduate school I went to work at Symantec (Fortune 500 in cybersecurity) and did Six Sigma, statistics, and analytics work for them. I started part-time freelancing in graduate school and have continue ever since.
Doing freelance work part-time really allowed me to springboard into it full-time once I’d built up the requisite income from freelancing to allow me to quit my full-time job.
During the first few years out of grad school, I really focused on building up my skills both through my full-time job and on my own (through online Courses at Coursera for example).
These days I freelance full-time and often taking on new projects with new clients forces you to learn new techniques and approaches to their particular problems.
What’s a typical day look like for you in your current data role? Which tools and languages do you use? Big team/small team/lone wolf? Remote/office based/co-working space?
I typically work from my laptop at my desk at home. These past few years I’ve mostly been a lone wolf but am really looking forward to working with more teammates at Mentor Analytics.
I don’t think I have a typical day but often I will sleep in until 9 or 10am and get a bit of consulting work done in the late morning/early afternoon.
Then, go to lunch, probably tutor some math/stats, go to the gym, get dinner, and possibly put in another hour or 2 of work around 10pm. Often I try to put in 2-4 hours of work per day.
My primary language is R but also do a fair bit of output in Excel as that is a more business friendly program. Many people who are not data scientist/analysts or generally people with less technical knowledge will work in Excel.
Hence, it is important to understand the language of business and be able to communicate with clients effectively.
I love R for it’s open source nature and the ability to find libraries on whatever algorithm I need for a current project.
I found you via an interview you did on the Fusion Analytics World blog and you’ve also been featured as a top-rated consultant on Upwork.
How important do you think it is for data professionals, at all stages of their career, to share publicly what they are doing and learning?
I think it depends on the individual and their goals. I think you can have a very successful data scientist/data professional that doesn’t publicly share their work.
Now, they probably will need visibility within their organization if they want career advancement, but they don’t need to share with the broader public.
I do believe there are benefits to sharing your knowledge and abilities as teaching is one of the best ways to learn. Additionally, you get some exposure and a chance that others in need of your skills will find out about you. So, I’d says it’s helpful but not essential.
Where do you see your own data career going next? Building on your technical skills or moving into a more management-based role now that you are running an analytics agency at Mentor Analytics?
I mostly see myself moving more towards a management type role although I’m sure I’ll still have heavy involvement with the actual analytics at Mentor Analytics.
For tackling bigger data problems having a team of specialists is needed. There is too much for any one person to know all the technical details to being able to bring together a team is key for doing an end-to-end data analytics pipeline.
End-to-end in this case would be from gathering and collecting data, to data processing/cleaning, to the actual analytic models and algorithms, and finally the reports/dashboards/tools that give the results and interpretations.
I’ll be looking to manage a team that can do all of those steps effectively.
Freelancing in any field brings its own set of hurdles to overcome before you get sitting down to do the actual work. From marketing to book-keeping to basic office admin. It’s not all coding and data viz, that’s for sure.
Would you recommend the freelance data science route to people considering it as a positive career move? How can they set themselves up for success?
There are definitely a lot of other aspects besides coding and the actual data science work that goes on when you are a freelancer. Freelancing can be great for some people and not so great for others.
You definitely need to find methods that keep you disciplined and on track to meeting your goals and deadlines. That’s very difficult for some people.
I personally, have no plans to change career paths and intend to make freelancing/self-employment my long-term career path. I enjoy the flexibility that is brings me and how creative I can be with all the new client projects.
Risk management is often key in getting set up for success. If someone is currently working a full-time job and looking to get into freelancing I would suggest starting part-time and getting some experience. It takes some time to build up a good clientele and find your particular niche.
Since Data Science is such a broad field figuring out where you can provide the most value and be most efficient with your time is going to be key. For example, I focus on data analytics and business decision making.
If you had a list of “best-kept-secrets” (websites, books, coaches) that have helped you, which would you recommend?
I personally got started using Coursera Specializations to really get a handle on the coding side of things. I completed 2 specializations (Johns Hopkins 10 courses in Data Science) and University of Washington (4 Courses in Data Science).
Overall, I’ve also completed around 30 courses on Coursera related to Data Science and Statistics.
There are many online classes from Coursera, Udemy, Udacity, Edx, etc. that can help get you started.
What is the number one piece of advice you give to aspiring data scientists?
Never stop learning.
Data science is a big and constantly evolving field so you need to keep up with all the developments that you can. By constantly learning you’ll not only develop key knowledge but also show that you take initiative and are highly curious.
Curiosity and persistence are going to be essential for any data project that you take on.