The rise of the data scientist

Part 2 of our series on trends looks at the Data Scientist profession.

We have a unique vantage point here at untapt; we get to work with a spectrum of companies, ranging from early stage startups to global enterprises. Across the board, our clients face the perennial problem of attracting and retaining the right technologists.

Recently, we’ve noticed new trends that present challenges and opportunities for our clients. We thought we’d share some common themes.

If you’d like to discuss this or any other challenges you’re facing with the tech talent market, please do get in touch – we’d love to have a conversation.

Trend 2: The rise of the Data Scientist

A few years ago, Big Data and Cloud computing capabilities were on every company’s wishlist; today, the attention has turned to Data Science. There’s an insatiable appetite for engineers that specialize in data analysis and modeling, as an increasing number of projects incorporate machine learning techniques.

This trend has resulted in a Cambrian Explosion of roles involving data manipulation, and some confusion in the industry about how to tell them apart.

At untapt, we believe that the new positions fall into two categories:

Data Scientist: typically someone with a math and statistics background who is proficient with AI / machine learning techniques.
They spend much of their time developing models using Python, Matlab, R or C++.

Data Engineer: typically a backend software developer, coding in languages like Python, Java, C++. The Data Engineer is familiar with the general concepts of machine learning without necessarily being a practitioner. They spend much of their time integrating models and deploying them to production. They partner closely with the Data Scientist.

In startups, you may find that one technologist is able to wear both hats. In large organizations, though, you may find entire teams of Data Scientists and Data Engineers working together.

These two roles command some of the highest salaries in the tech industry, and it’s common for talent in adjacent fields to look for opportunities to grow into Data Scientist or Engineer responsibilities.

Take note! These roles are not to be confused with some pre-existing functions:

Data Architect / Data Modeler: designs and creates database models
Data Analysis and Reporting: develops end user queries and reports, writes SQL queries and possesses a mastery of tools like Business Objects
Big Data Developer: writes software that operates over large datasets using big data frameworks like Hadoop

There’s plenty of ambiguity in these job titles, there are hybrid roles, and there are many important roles that we’ve omitted. If you have any thoughts, suggestions or questions about data-savvy developers, please don’t hesitate to get in touch – we’d love to hear from you!

 

Data Scientist

Stay tuned for more trends coming soon. In the meantime, if you’d like to speak with us about the challenges you’re facing, please do get in touch.

Ed is co-founder and CEO of untapt. A FinTech veteran and an Oxford alum, Ed was previously MD at JPMorgan and has held technology positions at IBM and various startups. Coder by day, Ed leads a double life as DJ after work. And if you happen to run into him, ask him how much he loves Seamless (hint: a lot).

Ed is co-founder and CEO of untapt. A FinTech veteran and an Oxford alum, Ed was previously MD at JPMorgan and has held technology positions at IBM and various startups. Coder by day, Ed leads a double life as DJ after work. And if you happen to run into him, ask him how much he loves Seamless (hint: a lot).