How to Build Your Career in Data Science

If you’re a Data Scientist and you’re setting your 2022 goals to improve and build your career, you’ve landed on the right page.

How to Build Your Career in Data Science
Photo by Lukas from Pexels


Building a successful career comes with a lot of benefits and life-changing opportunities. Unfortunately, we live in a society that is governed but also driven by money and social status. Working your way up the ladder is known to improve the quality of your life. It offers security and a feeling of accomplishment. People have been able to transform their life from zero to high-flying careers. 

If you’re a Data Scientist and you’re setting your 2022 goals to improve and build your career, you’ve landed on the right page. 


What is a Data Scientist?

A Data Scientist is someone who has been employed to analyse and interpret complex data. They are a mix of mathematicians, computer scientists, and somebody who is highly skilled at spotting trends. Being able to decipher large datasets, analysing and interpreting this analysis allows companies to implement these outcomes towards their short and long-term business goals. 


What Skills Will You Need to be a Data Scientist?

Soft skills are great and are very essential in growing your career. However, to define yourself as a Data Scientist, it requires hard skills such as analysis, data visualisations, machine learning, statistics, and more. Pairing with soft skills such as being a problem solver, eager and self-motivated learner along with critical thinker will help you excel in becoming a successful Data Scientist. 

The tech world is growing at such a fast rate, the only thing stopping you from building your career in this sector is the qualifications proving you offer these hard skills. 


Data Science Roles

Before you jump the gun and pick any qualification or course to take. It is good to understand that there are various roles in the Data Science industry, not just a Data Scientist. Below is a list of the most common careers in data science. 


1. Data Scientist

Let’s get the most obvious one out of the way to stop confusion. Data Scientists extract, analyse, and interpret large amounts of data from various sources. They will understand the business’s needs and use the data to develop a hypothesis, analyse the data, and explore different patterns with respect to the business agenda. 

They also use algorithmic approaches, artificial intelligence, machine learning, and statistical tools to further analyse the data, making it useful to a business. Business Analytics is also implemented in a Data Scientist’s role to present to companies how the data has proven to affect or benefit the company in the future.


2. Senior Data Scientist

Senior Data Scientists use data to guide and shape the company in the right direction, based on anticipating what a business’s future needs are. This may include directing, advising, and employing junior staff to steer them in the direction of the company's goals. Along with managing the data team, they also analyse data to resolve complex business issues and drive the development of the new standards, from prototyping to production.

The hard skills required for a Senior Data Scientist are similar to those of a Data Scientist, however with many more years of experience in each aspect such as Machine Learning, SQL, and different programming languages. They will also have exceptional interpersonal and people skills, as their role involves managing and mentoring high skilled staff. For data-dependent companies, Senior Data Scientists are like the captain of the ship. Without their expertise, knowledge, and experience, the rest of the team struggles to meet the business’s current and future needs.  


3. Business Intelligence Analyst

A Business Intelligence Analyst’s role is to identify potential opportunities for improvement, spot trends, and help the business grow by leveraging off the data. They can identify potential issues and present solutions, helping the company have a clearer understanding of where they stand. Their role is purely aimed towards improving efficiency, productivity, driving sales, and meeting the business’s short and long-term goals. 


4. Data Mining Engineer

Data Mining is the process of extracting, sorting through, and identifying patterns in large datasets that can improve a business’s system and operations. A Data Mining Engineer sets up and manages infrastructure for storing and analysing data. Their role can involve building data warehouses and organising data making it accessible for other team members. The key acronym for a Data Mining Engineer’s tasks is ETL: extract, transform and load. 

They will possess hard skills such as machine learning, statistics, database systems, and at the top of their list, SQL, which is widely used to store and access data. 


5. Data Architect

Data Architects create blueprints that data management systems use to centralise, integrate, manage, maintain and safeguard data sources, internal or external. Data Architects work closely with users, developers, and system designers, allowing employees to access specific and critical information in an allocated place. 


The Job Market

With technology continuing to be on the rise and millions of job openings in Tech and Big Data, the role of a Data Scientist is the 2nd best job in America according to glassdoor. Companies from all sectors, such as Fashion, Social Media, and Finance are using the skills of Data Scientists to stay one step ahead of their competition, reduce costs and potential threats to the company. Businesses rely heavily on data upon making informed decisions and effectively planning, therefore the need for Data Scientists will always be there. 


What You Should Know Before you Become a Data Scientist

Becoming a Data Scientist is challenging, it consists of a heavy workload, continuous learning, and some days of not understanding why the data is breaking or code that is not doing what you want it to do. Anything with great benefits seldom comes easy. 

The requirement to become a Data Scientist is difficult, however, once you have completed the right education, you will be able to reap the benefits. As data becomes an important element to different sectors, the Data Science skills become more transferable between these sectors. With the right training and qualifications, you could be a Data Scientist for a political company at the start of your career and work for a major FinTech company a few years later. 

Becoming a Data Scientist gives you the ability to move around whilst taking your hard skills with you, along with learning new ones.

Nisha Arya is a Data Scientist and freelance Technical writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.