Christian Finke founded JCFINCH as a boutique recruitment consultancy in 2017.
He has 10+ years of professional experience in digital consulting, finance IT, big data analytics & data science.
In the past, Christian has worked as a senior digital manager and financial IT specialist throughout Europe.
Amongst others, he has worked for Morgan Stanley, Accenture, Thomson Reuters, Bloomberg(RTS), Deutsche Bank, and Credit Suisse.
Christian’s academic background is in financial computing (MSc, University College London) and business studies (BA, University of East London).
1. What is JCFINCH?
JCFINCH is a recruiting consultancy in Düsseldorf specializing in data engineering, machine learning, and cloud architecture. For these areas, we recruit individual experts as well as whole teams. Our clients range from small startups to large DAX corporations.
Unlike other recruiting companies, JCFINCH recruiters do not come directly from HR but from technical areas. This means that we are actually the candidates that companies are looking for!
For example, as a senior manager at Accenture, I have previously implemented big data architectures for our clients and I know myself how to build up data teams. JCFINCH, therefore, combines its expertise in headhunting with technical data science know-how to find the right candidates.
Besides, our recruiters are paid on a retainer basis and are not contingency-based. Headhunters, who are paid on a contingency basis, often write to candidates en masse, as they receive a commission for each applicant they place. JCFINCH, on the other hand, works with a down payment, as we specifically search and place only a few, but the most suitable candidates.
Experience has shown that since our foundation in 2017, this approach has enabled us to respond better to our clients’ needs and to accompany their data journey.
2. Why did you move from consulting to headhunting?
My very first job was in capital markets trading, where I traded fixed income and derivatives products through our platform. Back then, I already used machine learning models to analyze historical data to identify trends and patterns.
What I particularly liked about this job was the combination of IT and finance.
After that, I changed to management consulting and I noticed early on with many clients that most projects do not fail because of the technology itself. It was always the people who made the difference.
I was a team leader at Accenture and I really enjoyed bringing the right people together.
During my time at Accenture, a good friend of mine was looking for a data scientist for his own startup. However, due to lack of experience and resources, he asked me to support him in recruiting. From this project, the idea for JCFINCH finally emerged.
3. How do you see the data science job market in the future?
If you look at the general data job market, from data engineering to business analytics, I believe that the job market will grow especially in Germany. Most companies have recognized the increasing relevance of data during the last 2-3 years and therefore try to establish internal data know-how by setting up their own departments and teams in this area.
I believe that graduates of technical studies with a data science reference will almost certainly find a good job in future.
These people are in high demand and there is a great lack of experienced experts.
However, the biggest differentiating factor between the candidates is work experience.
Especially for data architects, professional experience is extremely important. I see many graduates and young professionals who have a lot of theoretical knowledge in data engineering from top universities, but not yet the experience to handle larger projects on their own.
For data scientists I see things a little differently. In data science or machine learning creativity plays a big role and it often helps if the candidates are still young and not already bound to certain processes and industries.
The more unbisased data scientists are, the better.
Regarding future development, I personally believe that companies are centralizing their data engineering initiatives into their IT department, which will lead to a great demand for in-house positions.
Again, when it comes to data science, I see rather the opposite trend. I think that data scientists will increasingly act between different teams, as they should work as creatively and department-independently as possible. Another trend is the increasing use of freelancers for certain data science and machine learning projects.
4. What is the greatest challenge in finding good data scientists?
Finding the right data scientist or data engineer is not always easy for many companies, as some applicants are quite idealistic and picky. It’s important to the applicants for what kind of industry and company they provide their skills and services.
For example, some prefer e-commerce companies, while others prefer to work in the field of sensor technology. Therefore the search for candidates often resembles the search for the needle in the haystack.
Furthermore, the size and type of the data itself should not be underestimated if you want to attract data scientists to your team. Often the data treasure of a company fits on a single laptop, so that many companies are not yet ready for machine learning, but should hire a data engineer first.
These are the challenges and questions that employers should answer during the interview process.
It also often happens that candidates are over or underqualified for the advertised position. This is exactly where we as JCFINCH come in and help companies to identify the right qualification to finally recruit the desired data scientist.
5. What types of companies are popular among data scientists?
Obviously, the well-known tech giants like Google, Amazon, and Apple are extremely popular among applicants. Especially in the DACH market Amazon / AWS is in high demand because a large part of the development takes place in Germany and not only in California.
However, there are also many relatively unknown companies in the IoT field that are very interesting for data scientists. These are often small start-ups or medium-sized companies that were bought up by large corporations.
Another attractive industry is the financial sector with banks and insurance companies, as they usually offer large databases for analysis. Many data scientists see that their work has a great impact, especially in the insurance sector.
What companies should definitely bring along is an existing team of experienced data scientists and data engineers. Once, we had a client who faced great difficulty in attracting good candidates because the company was very small and did not have any data science teams. For data scientists, it’s important that they can learn from experienced colleagues and continue their education within the organization.
6. What invention do you hope to see in your lifetime?
I am fascinated by Elon Musk’s vision of flying to Mars and settling there. Of course, we still have many problems on Earth that we need to solve, but for me, Mars settlement would be the next great adventure for mankind.
7. If you could switch places with the CEO of a company for one day, which one would it be?
Richard Branson. His great strength is bringing people together and keeping them. He runs a multi-billion dollar company and yet he always seems to stay laidback and enjoy life…