Why I Joined McKinsey as a Data Scientist
Should you ever be a data science consultant despite the 80-hour work weeks?
Should you ever be a data science consultant despite the 80-hour work weeks?
Time really flies; we said goodbye to the crazy and unexpected 2021 and stepped into a new year….more than a month ago! Reflecting on 2021, I realized that I have left the consulting life behind for over a year now.
Looking back, joining McKinsey was one of the best moves I made for my career (so far) and I truly learned a ton about data science and career development in general from the experience (if you want to read about the lessons I learned about data science, read my previous article here).
So as a memo to myself and people who are interested in the consulting world, I want to share some thoughts in this article about why I initially joined McKinsey; in my next article, I will then share why I ultimately decided to leave. Hopefully, in combination these articles will provide some insights for those who are considering data science consulting as a career move.
Consulting is a great place to look for your passion and learn about different industries and from different industry experts
After being a quant researcher in the finance industry for almost two years, I realized that I loved the analytics and data aspect of my job, but I was not a huge fan of the finance industry.
So I decided to take my transferrable data skills somewhere else. The question was, where? I only knew what I DIDN’T want to do, but was clueless about what I DID want to do. Did I want to work with geo-spatial data? Optimize consumer marketing campaigns or B2B sales funnels?
Consulting is the perfect industry to go if you are in the same spot as I was — knowing that you want to work with data but don’t know what KIND of data you want to work with and/or which INDUSTRY you are interested in working in.
Because consulting firms serve a wide range of clients, no matter where your interests and passion lie, you WILL find something that’s for you. Even within the same industry, you will get to work with a wide range of companies in terms of size, maturity, culture and other dimensions.
Because experienced consultants have worked with dozens of companies in the past, they often have developed best practices/playbooks in a certain area. So as someone who’s new to the data science field, consulting is one of the best places to “learn the ropes”.
2. Consulting is a good way to pivot your career
The transition from a quant finance researcher to a data scientist was not an easy one despite the fact that there is a lot of overlap in terms of skillset; a lot of data science recruiters are unfamiliar with other fields (in my case the quant finance industry) and often don’t know how to correctly evaluate non-traditional backgrounds. Consulting hires from more diverse backgrounds than typical industry jobs, so it’s the perfect stepping stone and pivot opportunity if you want to make a career shift into data science.
Having a consulting background is definitely a plus if you are later applying for industry jobs; partially because of the prestige of most consulting firms and partially because of the skills and experience you get in a consultant role.
3. You learn to be agile, learn quickly and be a full-stack data scientist
In consulting, every project will be different and every new engagement is like getting a new job. As a result, you learn to be extremely agile and adapt to different working situations, managers, teammates and stakeholders quickly. Some clients use Python and some prefer R; some clients’ data is in databases and can be extracted through SQL, some have data only in CSVs or even PDFs. As a data science consultant, you often need to find creative ways to solve the seemingly impossible and up-skill yourself through learning on the job.
Because every project’s setup and team varies, as a data scientist, sometimes (if you are lucky) you get staffed with a whole team of data engineers; sometimes you have to BE the data engineer and try to process client’s “perfect” data from PDFs. Because of this, most data science consultants are “full-stack” data scientists who can work on the whole end-to-end process of data science projects covering data engineering, model building, all the way to tool building and insights generation.
These skills are extremely valuable for companies these days, especially for startups or companies that are just starting to build up their data science teams. Additionally, it was the perfect way for me to test out which career path in the data world I was actually interested in and wanted to specialize in (if you are not familiar with different data careers, read my previous article here).
4. Exit options are great and seeking them is even encouraged
Unlike most industry companies, which try to avoid churn like the plague, consulting companies aren’t too worried about their employees leaving. It might be obvious why this is the case — when consulting alumni leave to join industry companies, they become potential clients for the consulting company.
Every industry company hires consultants for something at some point; who better to vouch for you over your competitors than your own alumni?
Because consulting firms embrace the ultimate departure of the employee, they create a lot of channels like internal job boards, newsletter etc. for their alumni to share job opportunities outside of consulting.
Also most companies LOVE to hire ex-consultants because they have experience adapting and deploying data science solutions in different companies in various industries. And consultants are used to being thrown into the deep end and learn things from the scratch fast.
For me, consulting was a great way to figure out what I wanted to do next while keeping my options open, and once I had figured that out, the McKinsey brand, network and support (e.g. paid “search” time to find a new job) helped me line up my next opportunity.
5. The network you build is incredible
Since a lot of people leave consulting eventually, there are a lot of consulting alumni in industry companies’ management and leadership teams. So it’s likely that there’s already a McK alum working at your dream company when you are thinking about leaving consulting; and networking with alumni is always easier than cold-emailing.
What’s also common for ex-consultants is to eventually start their own companies, building on their experiences across various industries and opportunities they discovered along the way. If your dream is to eventually build a company of your own, it’s also likely to find like-minded co-founders in consulting (or you can find and join an early-stage venture that matches your interests through the McKinsey network).
Conclusion:
I think you should absolutely join consulting (McK or other companies) as a data scientist if any of the following applies to you:
You don’t know yet what kind of data you are passionate about as a data scientist
You don’t know which industry is right for you
You are hoping to pivot your career into data science from another field
You want to learn the tried and true best practices for data science (notice best practice doesn’t necessarily mean cutting edge; in fact, it’s almost always the contrary)
Consulting might NOT be for you if:
You want to go deep and specialize in a certain area of data science (e.g. you know want to ONLY focus on building ML models and be an expert in the area); this is possible in consulting, but might be easier to achieve in an industry role
You want to work on cutting edge methodologies in data science (I will explain why it’s hard for you to achieve this in consulting in my next article where I explain why I left McKinsey)
Don’t know what to read next? I might have some recommendations for you:
Productivity Tips for Data Scientists
How to work better, smarter but not necessarily harder as a data scientisttowardsdatascience.com
Top Qualities Hiring Managers Look For In Data Scientist Candidates
Some of these are arguably more important than writing efficient codetowardsdatascience.com
Avoid These Five Behaviors That Make You Look Like A Data Novice
And be a trustworthy, likable data partnermedium.com