Data Science Radar: How to Identify World-Class Data Science Capabilities

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Identifying World-Class Data Science Capabilities with Data Science Radar

Demand for workers with specialist data and analytic skills such as data scientists and data engineers has more than tripled over five years (+231%), according to a labour market analysis commissioned for Dynamics of data science skills, a Royal Society report. By contrast, the demand for all types of workers grew by 36% over the same period.

As organisations across all sectors embrace data-driven transformation, the need to identify such capabilities and upskill internal data communities has become ever more urgent.

Drawing on nearly two decades of analytical expertise honed from working with some of the world’s most complex industries, we have developed a comprehensive assessment of data science competence, Data Science Radar.

Data Science Radar assesses individuals and teams against 6 core data science traits which can help define the existing data science competencies, align strategic approaches to learning & development and resource projects to maximise value and help retain talent.

The six core capabilities of data scientists and consolidate the analytic skill required for data science teams include:


Data doesn’t sell itself; it needs a communicator to guide the way. Because of this, many great data scientists are master communicators.

As a Communicator you:

  • Are able to lead key business decision makers into an ongoing conversation with data, rather than carrying out ad hoc analyses.
  • Have a natural ability to communicate complex technical details to non-technical audiences
  • Have an understanding of the wider implications of the project, and so convey the key analysis insights to influence new business directions.
  • Understand that communication is a two-fold process: explain well, listen well.
  • Listen for business challenges, define requirements and clarify how data analysis can help.
  • Have the ability to speak a language that each of your stakeholders understands – even on projects using highly technical software and mathematical methods.


All great analysis starts with a dataset. Or rather starts with data in multiple locations, in different formats, languages and timezones.

As a Data-Wrangler you:

  • Understand that defining the question and the approach to creating insight stems from getting the data into a useable format.
  • Extract, manage and combine data from a variety of sources in a highly efficient manner.
  • Delight in the bottom-up approach that fully immerses you in problem solving, as it is in the detail where understanding of a system can be gained.


When you are at the forefront of innovation, often the tools needed to solve a problem simply do not exist.

As a Programmer you:

  • May already be a master of multiple technical languages and enjoy adding languages to your skillset.
  • Combine carefully constructed analysis workflow, with robust pipelines to automate as much of the process as possible, but enjoy building applications from scratch.
  • Understand the value of planning, and know that thinking through an analysis is more efficient and less error prone than starting an analysis and seeing what sticks.
  • Always ensure that your customers are not misled by your work by providing thorough reporting, including lists of assumptions and descriptions of algorithms along with your findings.
  • Use software development approaches such as unit testing and version control to ensure that costly mistakes are not caused by your work.
  • Your cool head and rational approach serve as a great counterpoint to team members whose focus on the big picture can lead to key details being missed.


Never satisfied with good enough, you find the best tool to aid with every challenge.

Since every challenge is different, it is often faster and more efficient to use technologies that have been created elsewhere, rather than reinventing the wheel.

As a Technologist you:

  • Are continually interested in exploring how evolving tools and techniques can add value to the data science workflow.
  • Modern multi-purpose programming languages provide the perfect environment to stand on the shoulders of giants and truly see further than others.
  • Know how to use many different technologies, allowing you to educate your team on possible ways to interrogate a dataset.
  • Are creative and relish using novel approaches when it comes to problem solving


By creating quantitative descriptions of your data, you create insight that is a key deliverable for your team.

As a Modeller you:

  • Interpret the meaningful reasons for features in a dataset.
  • Pay attention to the detail of underlying assumptions, limits and exceptions when describing a system.
  • Are familiar with a variety of mathematical methods for describing dynamic systems and are highly skilled in using software that implements these.
  • Use a variety of graphical and numeric techniques to verify that you are delivering a high quality result that can be used to predict and optimise future performance.
  • Are the ultimate investigator – when you’re on the team, if there is information that can be gleaned from a system, you’ll find it.


You convert information into a landscape that can be explored with the eyes to create an information map.

This skillset is absolutely indispensable for organisations that are lost in information. That said, you don’t like to be kept on a short leash.

As a Visualiser you:

  • Welcome the chance to experiment and explore all possible data opportunities and the new product avenues it can open.
  • Have a creative urge to go beneath the surface to uncover creative data solutions that can permeate the entire business.
  • Are imaginative when conveying information visually, and use a variety of graphical tools to ensure the patterns you find are presented coherently; both to internal and external customers.
  • May be the most important storyteller in your the team – afterall, seeing is believing.

Personal Radars

Following an initial assessment, this SaaS solution provides each team member with their own personal radar, highlighing their personal skills and strengths, whilst highlighting a path for potential learning and development.

A typical Radar for an experienced Consultant might look like this:


Whereas a Junior Data Scientst’s might look like this:


Doug Ashton, our Principal Data Scientist shares his Radar and key traits as:

  • Programmer
  • Communicator
  • Modeller-Technologist

Doug explains how the Radar has helped his work at Mango Solutions:

What impact has the radar had on your recent work?

“Thinking about data science through the lens of the radar has helped me organise teams. Between us, and as you would expect in our Consultancy, we can create a pretty full radar by combining consultants’ skills. Rather than attempting to do everything myself, I look to the radar for colleagues who specialise in that area. A most recent example included working alongside a specialist visualiser, helping to build a vocabulary of graphics for a client to create a consistent look and feel across graphics. Working alongside, gave me opportunity to develop my own skills”, said Doug.

Which parts of your radar would you like to improve the most and why?

“I consider myself an experienced modeller but one thing the radar has reminded me is how big the modelling world is. It’s more than just one type of machine learning method. So, I would like to constantly improve and expand my modelling skills and understanding across a broad range of areas. This is vital to keep up with the pace of advancement in data science. Rather than try to improve my visualiser score I prefer to focus on my strengths and to make best use of some of my expert visualising colleagues. Of course, that doesn’t mean I don’t enjoy the odd ggplot from time to time!”.


Know your team’s skills and capabilities

A thorough understanding of capabilities and skill level mapped against these traits for the team, allows Managers to qualify strengths, areas for development and enables analytic leads to make effective decisions for delivering data-driven value.

Building a centralised hub of excellence for best practice and stakeholder collaboration for a data community is key to data -driven transformation and avoids what might otherwise be potentially siloed and untapped resource.


Want to learn more?

Data Science Radar is a software solution that can help you define and build the right data science capability to align to your strategic objectives. To request a demo click here.