Essential Qualities of a Profesional Data Scientist

Source: Deep Learning on Medium

Data Science Secrets

Essential Qualities of a Profesional Data Scientist

Qualities employers look for in the ideal candidate

Companies are on a hiring spree for data analysts roles. The stakes are high and the compatioiton is tough. How can you stand out from the crowd?

Here are the top traits that employers look for in the ideal candidate.

Hard skills:

Statistical Expertise

All models developed by data science require statistical concepts and models such as regression, optimization, clustering, decision trees, random forests, etc.

Why is it important?

When designing a model, you need to uses statistical concepts. Although there are plenty of ready made models available, each project is unique and will require tweaking, that is when a knowledge of statistics is vital. It gives you the freedom to design and implement custom models.

Technical Expertise

Top-notch data scientists know how to write code and are comfortable handling a variety of programming tasks. To be really successful as a data scientist, one needs to know how to deal with large volumes of data, work with real-time data, cloud computing, unstructured data.

Why is it important?

Rather than hire a data scientist to design the model and a developer to deploy the model separately, employers prefer to hire one individual who can handle both tasks.

Machine learning & Deep learning Concepts

Machine learning and deep learning offer their own set of benefits and limitations. It is upto the data scientist to know where they are applicable.

Why is it important?

Understanding the concepts of machine learning and deep learning will aid the data scientist in making the right choice when it comes to models, algorithms etc. Thus getting better results.

Data Architecture

It is imperative that the data scientist understand what is happening to the data from inception to model to business decision.

Things can change within the architecture. Without understanding its impact on models the data scientist will end up going in circles without achieving the goal.

Why is it important?

By understanding existing architectures, a data scientist can save valuable time, without re-inventing existing architectures, to actually work on the data. They can even build on existing architectures.

Research Experience

Research comes in handy when presenting findings to colleagues and defending suggestions on what move the company should make next.

Why is it important?

During research, the data scientist essentially goes through all the steps of a data analytics projects, from data preperation upto the final inference. They even present their findings in a way that anyone can understand and learn how to create reproducable code. All of these skills are a bonus in the industry.

Business Acumen

A sharp data scientist needs to understand the concepts of analyzing business risk, making improvements in processes, and how systems engineering works.

Why is it important?

Having a business acumen helps you understand the problem from the industry’s perspective. This makes it easier to develop a solution that truly fulfils the comapny’s needs. It also helps to understand the industry you are working with. Example: Medical, e-commerce etc.

Photo by Mimi Thian on Unsplash

Soft Skills


Does not have biased opinions of the data, outcome, model development etc.

Why is it important?

It is imperative not to have biased opinions when conducting analysis as it could affect the results.

Self-motivated team player

Data analysts collaborate with people in a variety of positions to get the job done. They work with data scientists to determine what types of questions can be answered through data analysis. They also collaborate with web developers to ensure that their company’s website is designed to efficiently capture the data they need.

On a big-picture scale, data analysts collaborate with company leaders to determine how the latest data insights can guide the company in moving forward toward its objectives.

Why is it important?

As a data scientist you need to be able to work alone, but to get the job done you will need regular inputs and communication from other departments of the company.

Detail-Oriented & Methodical

A good data analyst understands the nuances of the data they’re working with. For example, it’s important to understand the difference between null values and blank fields and how to extract data accordingly.

Why is it important?

Attention to detail helps the data scientist spot potential problems, bugs, the requirment for more data etc. It makes the data scientist’s work look more polished too.

Organizing projects and tackling them in a methodical process ensures a smooth workflow.


Top-notch data analysts look for the meaning — the “why” — behind the numbers. With an insatiable drive to discover actionable insights, they constantly ask questions and search for solutions.

Why is it important?

This helps the data scientist ask the questions that no one else is asking and unearths the underlying problems that would not be found otherwise. Most often the root cause of a business problem is completely unexpected.

Critical Thinking

Data scientists need to be able to apply objective analysis of facts on a given topic or problem before formulating opinions or rendering judgments. They need to be able to translate a business issue into an analytics problem.


A data scietist needs to see the same problem with fresh perspectives, understand how to properly present their findings. Capable of spotting patterns — the ability to spot trends or themes in the data.

Why is it important?

When plotting graphs and preparing visuals, creativity helps to make visually pleasing charts. Being able to spot patterns is a critical skill in data analysis, so that unexpected problems can be nipped in the bud.

Effective communication skills

The technical term for it is ‘Data Story Telling”. A data scientist needs to be able to translate their findings into inferences that individuals with technical skills as well as non-technical skills can understand and benefit from.

The data scientist needs to be able to present insights depending on their relevance to different departments.

Why is it important?

Communication skills tie bak to the teamwork skill mentioned earlier. The data scientist articulates analytical results in business language for each department.

Continuous learning

New technology, tools and theories require the most talented data analysts to stay up-to-date by attending industry events, reading widely and making connections.

Why is it important?

By staying upto date, the data analyst is able to suggest new technology to their company which will improve workflow and give better results. They will find it easier to adapt to new technology which the business introduces.