I wanted to write this post today to help explain a new trend we are seeing in the marketplace for companies wanting Data Scientists with solid coding backgrounds. This is very different than the classic profile of a Data Scientist having a strong statistical or mathematical type background.

Well, how did we get here?

What has transpired to get us to this point and where do we see things going from here?

We have been told now for years that data is new the gold or oil. But what does this exactly mean?

How are companies really tapping into this new strategy to be more competitive, efficient, and customer-centric? The short answer is 76% of all businesses have at least started or in the process being more data-driven. Today, more and more companies are realizing the role of the data scientist is not just hype but there is true value in this keen eye, data craftsman/woman.

So, you ask how are Data Scientists making a measurable impact on the bottom link?

The short answer is Machine Learning.

Algorithmia’s Third Annual Survey, 2021 Enterprise Trends in Machine Learning

Here are examples of ways ML is being utilized in our everyday digital interactions:

  1. Smartphones (to name a few)
  • Facial recognition 
  • Deepfake apps
  • Language translation

2. Transportation Optimization

  • Optimizing supply chain
  • Managing inventory

3. Internet Websites

  • Recommendation engines

4. Sales and Marketing

  • Personalized product suggestions
  • 1 to 1 marketing campaigns
  • Increasing customer loyalty
  • Retaining customers
  • Improving customer engagements

5. Security

  • ML-enabled activity anomaly detection
  • Secure Identification Management

6.  Financial Domain

  • Risk analytics
  • Fraud detection
  • Potential client identification

So let’s get back to our Data Scientists and how they fit into the picture now and what new skill set is being requested of them in today’s more mature data-focused companies.

Because so many Machine Learning libraries today are utilized by using Python, this is one of the main drivers for companies desiring more “coding” Data Scientists and in particular Python proficiency. 

Additionally, they are requiring not just the ability to build the ML models but also any experience in deploying the ML models into production environments would be ideal.

Here is a list of the top 20 Python libraries for Machine Learning and Data Science according to KDNuggets 2021:

Increasingly, we notice the ML Engineer role disappear with one some of the tasks being offloaded to Data Engineers and others to Data Scientists. Essentially in today’s job market you cannot really expect to be called a “Data Scientist” or “Data Engineer” without having some level of proficiency in ML.

I hope this post has been informative and provided more insights in the current marketplace at the end of 2021. Feel free to send me a note ― rassul@datatalentadvisors.com ― if you would like to collaborate, or write your comments down below.

Rassul Fazelat, President & CEO, Data Talent Advisors