The buzz around data science services seems to be growing every day. Big Data, machine learning, analytics - all these things are trending and getting more and more interest. Since 2012, when Harvard Business Review named data science ‘the sexiest job of the 21st century’, the number of job postings is multiplying . For instance, the Royal Society reports that the number of UK job postings related to data science services went up by 231% between 2013 and 2018 . As of September 2019, LinkedIn lists almosts 29,000 jobs in the US, over 20,500 offers in the EU, and a little over 6,000 opportunities in the UK.
So what data science skills are companies looking for in 2019? An analysis  shows that machine learning and analysis, along with statistics and math, are the most commonly mentioned data science skills found in the job descriptions.
Also, a good share of companies is looking for data scientists with the knowledge of deep learning and NLP. If we take a look at the data science technologies that are in high demand, an overwhelming majority of vacancies list Python as the top technology, followed by R, Hadoop, Spark, and Java.
So with that many data science technology options and extremely high demand for data scientists, how do you find a great provider of data science services? Here are 5 guidelines to follow.
Clarify your business goals
Before you rush into any decisions or even begin the hunt for a great data science company, take a step back and analyze what you need. Why do you need data science services? Do you need Big Data analytics or business intelligence? Can you work with a third-party solution integration, or will you need R&D and customized solution development?
If you have a team of experts within your own company, it might be just enough to hire an in-house expert. However, If you need a project launched from scratch, or you don’t have the expertise needed to deliver a solution, or you have a difficulty establishing clear business KPIs, you need to consider outsourcing and finding a reliable partner.
Choose the location wisely
Since the demand for qualified data science developers is growing, many companies find it hard to scale up locally. However, outsourcing comes in many different shapes, so everyone can find what they are looking for: onshoring, nearshoring, or offshoring.
What questions to ask yourself while choosing the destination for the outsourcing of data science services?
- Is this country known for high-quality outsourcing services?
- How many data science developers are there?
- How many companies there have expertise with data science services?
Analyze the tech talent pool within the chosen location
While looking for data science developers, pay attention to the number of skilled experts within the country as well as the competition for this tech talent. For instance, Germany has over 22,000 professional data scientists, but the number of tech companies fighting for each expert is enormous. On the other hand, outsourcing to locations like Eastern Europe helps solve this issue as the number of tech giants is lower. Let’s take a look at the number of professional data science developers in this region.
Poland has nearly 4,000 experts working as data scientists, while Ukraine has the second largest pool of data science developers in Eastern Europe. Both these countries have considerably lower competition for these experts.
You should also look at the availability of different events, communities, and courses for developing expertise in data science and related fields is necessary to stay competitive in today’s high-tech and rapidly evolving society.
Establish clear criteria for the vendor selection
The choice of the data science services vendor depends on the type of data analytics you need to build, how it will be handled, and whether you need ongoing support. Be clear about the key factors that can affect the selection process:
- Tech and industry expertise;
- Relevant experience;
- Company size;
- Industry recognition, awards, and acknowledgment;
There are hundreds of companies that provide data science services, so make sure to take a closer look at the vendors that match your needs. You can also use our step-by-step guide on the vendor’s selection process.
Determine the tech stack for your business case
If you are not clear about the technologies that will power your data science project, you might need to consult your vendor or have a product discovery phase. For instance, you may need expertise in a variety of technologies and tools to efficiently extract, clean the data, apply the algorithms to it, and visualize the findings. They may include R, Python, Java, Scala, Hadoop, Cassandra, Matlab, Tableau, etc. Consult your vendor to determine which stack will allow you to get the most of the data you have.
All in all, the amount of data collected every day, the demand for high-quality data science services will grow instantly. In 2019, data science became a crucial skill for developers and managers across many industries. That is why more and more companies look for a reliable data science service vendor to help them effectively transform raw data into valuable insights. These tips can help you move your business to the next level and effectively leverage data science as a service.
- Dynamics of data science skills: How can all sectors benefit from data science talent by The Royal Society
- Hale, J. The Most in Demand Skills for Data Scientists for kdnuggets.com
- DavenportD.J, T. H., Patil, McAfee, A., & Brynjolfsson, E. (2017, May 26). Data Scientist: The Sexiest Job of the 21st Century.