The 3 must-have skills you didn't know
data professionals need

In 2020, the World Economic Forum published “The Future of Jobs Report”, indicating the top declining and emerging roles in the world. According to the publication, while 75 million (mostly) clerical jobs will decline, 133 million new roles will emerge. Can you guess which roles are dominating the top 10 emerging roles? That’s right – data analysts, data scientists and other data specialists.

That being said, it’s no wonder that the internet is filled with articles revealing must-have skills for data professionals – “Top 3 Python packages”, “Best tips for efficient SQL” and “You must use version control” are just some of the most popular articles in blogs such as Medium. The focus is often primarily on hard data & tech skills – such as how to code, how to share code and new emerging algorithms. And indeed, without skills such as coding, algorithmic understanding and best-practice technologies, you can’t be a successful data professional.

Key learnings for Data Scientists


But focusing on the hard skills is not enough. To become an all-round data professional that can create business value, we see four main knowledge areas that you need to develop:

  1. Machine learning & statistics – Understanding of modeling techniques, modeling approach, knowledge of risks and responsibilities in model development, and the ability to link business problems to analytical solutions.
  2. Data & technology – Knowledge of architecture and technology to access and store data, quality management, the practical use of analytical tooling, and the ability to develop production-ready solutions, keeping the business change in mind.
  3. Impact & opportunities – A methodical approach to identify the impact of AI opportunities, and to design impact-driven change programs.
  4. Leadership & change – Ability to shape and lead change programs, utilizing personal strengths, create enthusiasm and shape the company culture.

In this article, we will share some of the must-have skills for data professionals in these four domains, as experienced by two data scientists who have completed the AI & Data Talent Program (AIDTP) of GAIn® (the Global Artificial Intelligence Network). This 3-year program is designed to shape talented individuals into all-round data professionals, capable of fulfilling “the promise of AI” (More about AIDTP and GAIn can be found at the bottom of the article).

Roman Kats (Israel Discount Bank) and Rotem Pinchover (MAX credit card company) are Data Scientists at two of the leading financial institutions in Israel. They are eager to share tips from their combined experience of over 10 years in the industry.

Let’s get started!

The fundamentals: Build great modeling & tech skills

To re-emphasize, getting the fundamentals right is crucial. This means you need to build a solid knowledge on modeling, technologies and coding. There’s no need to remember everything by heart, but to ensure you have easy access to best-practice solutions and a clear idea how to use them for different problems.


Advanced Model Optimization training


Rotem: “The data science field is very broad, and consists of many technologies and algorithms. The more algorithmic and technological understanding we have gathered in our department, the better our models have become – accuracy is increasing, code is more readable and better technologies are adopted.”


Rotem’s advice: “Have your best practices clear and available. You don’t need to remember all theory by heart, but you do need to organize it in such a way that you can quickly access it. I have a few books and my GAIn workbooks at my desk. A few weeks ago I needed to reduce the number of dimensions when working with an incredibly large data panel, so I turned to the Advanced model optimization workbook and quickly extracted a recommended method to do so.”


But apart from these fundamental modeling & tech skills Roman and Rotem also named three must-have skills you might not have thought about immediately.


Skill #1: Impact-orientation

Understand that your role is to improve business performance and create organizational change, not build the fanciest models. You do this by focusing on creating immediate impact and iterate from there. Additionally, you need to learn how to convince stakeholders to trust your solutions. Eventually, whether your model will be used or not, depends on the trust of your stakeholders.

Data-Driven Communication training

Roman: “One of the biggest changes I’ve managed to do is to start focusing on impact, rather than results. The understanding that my role is bigger than how I’ve used to perceive it made all the difference. As a data professional, you are hired to change business processes and help your organization grow. Once you really internalize this change in your mindset, you start focusing on the right things.”


Roman’s advice: “Instead of ensuring your model is able to handle all scenarios and get the last 5% right, you should start focusing on the question how do I make immediate organizational impact with the good result I already have, and further improve from there?”


Roman also sees great importance in leadership skills:

“Impact also means that at the end, the people who are using your models are not from the data community but end-users like business people. Therefore, convincing non-technical stakeholders to trust your model and start using it is one of the most crucial skills to help you pursue organizational change with your models. To do that – focus on story lining, visualization and presentation skills. A good story with professional data visuals can go a long way, perhaps further than an additional day on model improvement”


Skill #2: Shape your professional attitude based on your core personality traits

In order to be effective, you need to understand who you are and how to position yourself among team members and stakeholders. Proactively ask for feedback to better understand how people see your core advantages and challenges.

First contact with your stakeholder training

Rotem: “When I joined the program I was a fresh graduate student landing my first job in the field of data science. I came in tabula rasa mode, eager to learn and start my career. During the bootcamp (the first six weeks of the AIDTP program) we learned about hard skills such as classification, regression etc., but what struck me as the most valuable learning was the “First contact with your stakeholder” workshop, during which we were introduced to a professional personality framework. Understanding my personality traits and how they are related to my daily job was a real game-changer for me. The ability to better understand how other people see me and what my core advantages are, helps me develop my professional attitude and focus on the roles where I contribute the most”.


Rotem’s advice: “Understand your core advantages and challenges by continuously asking for feedback. You might be surprised by what you will find out about yourself. Personality frameworks are also a powerful tool to get to know yourself.”


Skill #3: Keep an open mind and stay curious to learn more

Proactively assign time to learn different types of new skills to become an all-round data professional.

Rotem: “People have the tendency to think they know more than they do, especially when they are new to a field. Going through two years of education, combined with a daily job, makes you humble about the skills you actually own, and the skills you can continuously improve.

The benefits of having a learning attitude are not only that you have more knowledge, but it makes you more curious, more critical and more willing to listen to new ideas. I think that a learning attitude is what helps you most in making big steps in your career.”


Rotem’s advice: Proactively assign time to learn new skills, and make sure that your focus is on different types of skillsets – to make you an all-round data professional.


That’s it – The 3 must-have skills you didn’t know data professionals need.

For more information on GAIn, AIDTP and unique content on AI – follow us on GAIn LinkedIn and visit the Career Tracks section on our website.


What is the AI & Data Talent Program (AIDTP)?

The AIDTP is a 3-year educational program focused on kick-starting careers in data departments within organizations by building up the knowledge, practical skills and experience required to create real impact with AI and data. Companies who participate in the program strongly believe in the power of ongoing education as a lever for growth. The AIDTP consist of four blocks starting with the AI Foundation. After this, participants take part in a 6-week full-time bootcamp, which is followed by the Expert-program where participants can choose a track customized for either data scientists or AI engineers. The AIDTP is finalized by the Master-program consisting of 8 follow up weeks and a 3-month exchange project, in which participants can really specialize into a leading AI role.


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