Don’t Become a Data Scientist Or Should You?

Becoming a data scientist has been branded as one of the sexiest jobs of the 21st century, but this doesn’t mean you should become a data scientist. Or should you? 

The U.S. Bureau of Labor Statistics sees strong growth for data science jobs skills in its prediction that the data science field will grow about 28% through 2026. While it probably would be unwise to be against data science, we need to look beyond the role and definition of a data scientist as there are many more closely related and market-relevant roles that you can grow into such as research engineer, machine learning engineer, AI product owner, and more. 

In this article, the goal is to help you see beyond the title of a data scientist. At the end of it, you will have learned about the skills you need to develop for a successful career, as well as alternative or complementary career paths you can take.

Let’s get started. 

Defining the Data Scientist Career Path 

Data science is an interdisciplinary field that combines techniques from other fields to extract knowledge and information from raw, unstructured data. Data scientists are experts responsible for creating code and using this code alongside statistical methods to draw insights from data. 

While in school, data scientists are prepared for various roles that involve:

• Creating machine learning models for solving real-world problems
• Using programming languages to extract large volumes of structured and unstructured data
• Cleaning data to prepare it for modeling (predictive and prescriptive) using analytical and statistical and machine learning
• Identifying ways to handle missing data using exploratory data analysis
• Identifying new algorithms and building programs to solve existing problems
• Finding data patterns that provide insights
• Creating algorithms and data models to handle data
• Deploying data tools 
• Communicating recommendations to senior management and clients 

Data scientists also work with other teams and departments within an organization. These departments include sales, marketing, and design. Working with these departments allows data scientists to understand the needs of each segment of the business, thus developing solutions that adequately address those problems.

Why You Should Not Become a Data Scientist

Before we go any further, becoming a data scientist can be a lucrative career. Becoming a data scientist can be so lucrative that the market has become so competitive, especially if you want to work in the big tech companies. 

You might not be happy working as a data scientist

That said, most data scientists are just not fulfilled at their jobs. This reason is common in the tech industry as well – responsibilities do not align with expectations of what a data scientist does. 

Due to the buzz around making use of big data to draw business insights, most businesses have hired data scientists impulsively, without the right infrastructure to support these experts. So what happens is that, instead of spending their time writing machine learning algorithms, they spend their time creating analytics reports or establishing data infrastructure in their new role. 

Studies have shown that data scientists spend 80% of their time cleaning and organizing data. It gets worse as there’s no end in sight that companies will start giving data scientists the responsibilities that are meant for data scientists. 

Nothing wrong in cleaning and organizing data; in fact, it is a key skill but as a data scientist you want to also deploy models, and make an impact on the business and society. 

Your role might be vague and unpredictable 

It does not help the situation that there’s no consensus on what data science represents or what the roles and responsibilities of a data scientist are. This means that as a job applicant, you may be involved in developing ML models for business A, but once you move to business B you deal with other tasks unrelated to your expertise such as working with financial spreadsheets. 

The popularity of data scientists and other roles such as AI specialists grew from the hype around the two. Everyone wanted to be the leader in this emerging field.

You might be expected to deliver unreasonable results

In the same spirit, businesses will hire a data scientist, expecting an immediate return on their investment. In reality, data projects are time-consuming. They can take months of trial and error before the desired outcome is achieved. 

This, however, does not sit well with business leaders who want to see results, which can leave you, the data scientist, feeling unappreciated and misunderstood. 

Data science is a relatively new field that is evolving constantly, which means as a data scientist, you have to constantly learn to keep building your skills. What you will find for most data scientists is that you need skills in computer science, statistics, and business. This means you need to put in the work to learn from these diverse fields, constantly. So, if you want to be in a career that remains relatively unchanged, data science is not for you. 

Basic Skills You Should Develop for a Meaningful Data Science Career 

Just earning your bachelor’s degree will not get you into that big tech company you have your eye on. Employers expect you to demonstrate the skills that make you a great choice for their role. These skills are developed through learning and constantly practicing on data science projects.

The data science landscape is always changing, which means you have to evolve with the industry by constantly educating yourself on emerging technologies and tools. 

Let’s look at some of the technical skills you need to succeed as a data scientist:

1. Machine Learning 

Depending on the company you work with and the amount of data they handle, machine learning is an essential skill for you to succeed as a data scientist. Machine learning skills are essential in building predictive models. 

Some of the machine learning skills you should develop include:

• Anomaly detection
• Recommendation systems
• Decision tree
• Regression
• Time series prediction model

Learn more: 10 ML Algorithms for Data Scientists (+ Real-World Case Studies)

2. Math 

You cannot become a data scientist without a math foundation. Here are the specific math skills you should develop:

• Statistics: statistics concepts such as mode, median, mode, standard deviation, and distribution are essential for data scientists. Data scientists should also understand data sampling techniques, avoiding bias in experiments, descriptive statistics, and inferential statistics. 
• Probability: understanding probability helps a data scientist identify meaningful trends from data. Some concepts of probability that you should understand include central limit theorem, Bayes Theorem, standard errors, random variables, expected values, and independence. 
• Linear algebra forms the foundation of algorithms. This knowledge is critical when you want to focus more on the machine learning side of data science. 
• Multivariate calculus: calculus concepts such as gradient, derivatives, mean value theorems, limits, Taylor series, beta and gamma functions, and product and chain rules are essential in logistic regression algorithms. 

The foundational math skills you need are taught in high school math. However, you can still explore university courses, online classes, or self-study using the relevant textbooks to advance your skill set. 

3. Programming 

Data scientists interact with programming languages all the time in their work. Most data scientists are proficient in python, which is the most commonly used programming language. Here are the programming languages you should learn:

• Python: Python is the go-to programming language for most data science applications as it is easy to deploy and comes with an active community. When learning this programming language, brush up on the fundamentals, python libraries, and reusable code. 
• R is another important programming language every data scientist should know. This open-source programming language is used for statistical analysis.
• SAS

When learning programming languages, you should start by exploring the career paths you want to take, and the industry you will work in. For example, Tech companies mostly use R and Python while the financial industry goes for SAS and R. 

4. Analytical Tools 

Data scientists work to derive meaningful insights from data. They achieve this by using analytical tools such as SQL, Spark, and Hadoop. You can learn these tools from online tutorials. 

5. Data Visualization 

Data scientists are hired to help businesses make sense of data. In its raw form, data is meaningless to the organization holding it. This is why organizations need data scientists to use various techniques to analyze and visualize this data. 

Data scientists need to learn data visualization skills to help them turn data into understandable charts, graphs, and other visuals for decision-makers. The data visualization tools you should learn include Tableau and Power BI. 

6. Data Wrangling 

When data scientists collect data, they might come across certain data that needs cleaning. By developing data wrangling skills, you can address the common issues of data such as:

• Incomplete data
• Missing values
• Date formatting
• String formatting

Once this data is clean, you can proceed to draw insights from clean data. 

In addition to these technical skills, data scientists must develop certain soft skills to help them work with other professionals and clients. 

7. Model Deployment / MLOps 

We mentioned earlier that data scientists must have machine learning skills which they use to build machine learning models. Model deployment skills are also essential in putting the models you build into real-world use to solve a business problem. 

Read more: How to Deploy Machine Learning Models in Production (+ Real-World Case Studies)

Here are the top soft skills you need to succeed as a data scientist:

1. Critical Thinking 

Critical thinking is a valuable skill in data science. Critical thinking allows data scientists to objectively identify problems and the resources required to solve these problems. They can also look at a problem from other perspectives. 

2. Effective Communication 

While data scientists are involved in the hard work of collecting, cleaning and visualizing data, they must then communicate their results to key stakeholders. This calls for effective communication skills so that they can explain their findings to non-technical audiences. 

3. Collaboration 

Data scientists work with other members of the data team, software engineers, IT staff and managerial staff. Therefore, to succeed in their role they must learn to offer and accept input from other members of the organization towards achieving a company’s goals. 

4. Problem Solving 

Data scientists are problem solvers. Data scientists should have problem-solving skills to help them identify problems and potential solutions, and exploit the resources they have to solve these problems. Problem solving also entails identifying the most effective methods to find the right solution for each problem. 

5. Intellectual Curiosity 

Data scientists must always maintain a curiosity that helps them go beyond the questions that were initially asked, and the assumptions made with certain data. 

6. Business Sense 

Data scientists exist to solve business problems with data. This means you should be in a position to identify a business problem and why they need to be solved. You should also be capable of translating data into results that work for that particular business. 

Related Data Science Career Paths 

Becoming a data scientist is just one of the parts of a data science career. You can explore a variety of lucrative data science careers that take advantage of your skill sets and knowledge. Here are some of the data science careers you should explore instead of becoming a data scientist. 

1. Machine Learning Engineer 

Machine learning engineers research, design, and build artificial intelligence systems. Machine learning engineers work with other data scientists, analysts, administrators, and architects. Some of their responsibilities include:

• Designing machine learning system 
• Statistical analysis
• Verifying data quality 
• Improving machine learning models based on results
• Expanding machine learning libraries

2. Machine Learning Scientist / Research Scientist

Machine learning scientists are researching and developing algorithms for adaptive systems in AI. Some of the responsibilities of a machine learning scientist include:

• Designing and implementing machine learning models
• Matching algorithms and models using probability 
• Researching and developing scalable ML solutions
• Collaborating with the data science team and other departments in the organizations 

3. Data Architect 

A data architect is an IT specialist responsible for designing and managing data systems. A data architect is responsible for:

• Creating and implementing procedures and policies for data quality and accessibility
• Analyzing, planning, and defining an organization’s data architecture framework 
• Working with other departments in the organization to create and implement data strategies that help achieve business goals
• Managing a business’ end-to-end data architecture 
• Planning and executing big data solutions 
• Managing the secure flow of information within an organization 
• Conducting regular audits of data management systems to identify performance or security issues. 

The difference between data architects and data engineers roles lies in their primary responsibilities. Data architects design the vision and blueprint of the organization’s data framework, while the data engineer is responsible for creating that vision

4. Data Engineer 

A data engineer is an IT specialist who finds trends in data and develops algorithms to convert raw data into useful information for an organization. Data engineers require a deep knowledge of programming languages and SQL database design.  

Data engineers should also have effective communication skills so they can work with other teams and stakeholders in the organization. 

Data engineers can be generalists, pipeline-centric, or database-centric. Generalist data engineers handle each step of data including managing and analyzing data. Pipeline-centric data engineers work with data scientists to use the data collected. 

Database-centric data engineers manage the flow of data in an organization. They handle databases, work with data warehouses and develop table schemas. 

5. Business Intelligence Developer 

A business intelligence developer is an engineer who works with business intelligence software to interpret and present data for a business. They will create tools and refine current methods to improve a company’s processes. 

Some of the responsibilities of a business intelligence developer include:

• Creating and updating business intelligence solutions
• Developing technical questions and exact search queries
• Simplifying valuable business data 
• Curating data
• Creating data visualizations 
• Documenting processes for the future
• Troubleshooting BI modeling issues
• Backing up and securing data 

6. AI Product Manager

AI product managers are in charge of the AI product development process from conception to the launch of an AI product. AI product managers work with teams of diverse skills sets to research, conceptualize, develop and deploy viable AI products to meet different business goals. 

Some of the responsibilities of an AI product manager are:

• Identifying the questions to ask clients or key stakeholders to understand the type of AI product they need
• Working with key stakeholders in a business to understand business needs that an AI product will meet
• Communicating business needs and project requirements to the data and project teams
• Problem mapping 
• Identifying gaps between business goals and performance 
• Developing AI product timelines
• Identifying new sources of data
• Researching, gathering data, and visualizing findings during the product development process

How to Choose the Right Data Science Career Path for You

With so many data science career paths to choose from, how do you find a path that works for you? 

1. Experiment 

Data science careers combine engineering and business concepts and skills. Some career paths lean more to the engineering or business side, while others are a perfect blend of both sides. The best way to know what works best for you is to experiment.

Start with the skills or strengths you already have and work from there. For instance, if math and statistics come easy to you, you may consider a path on the analytics side of data science. You can experiment with different data roles to see what role suits you best. 

A good way to experiment is to look for projects in any data science area and work on those for some time. Omdena offers a unique opportunity to build a variety of skills working on real-world projects which take two months to complete. 

Through experimentation, you will learn what comes to you naturally. 

2. Reflect on What is Fun for You 

Once you experiment with a number of roles, you should reflect on the pros and cons of each role. What tasks or projects were most enjoyable to you? Based on your results, you can choose a career path that has more of the tasks that were fun for you and less of what felt laborious. 

3. Choose a Path and Go Deep

At this point, you should have now figured out the career path that suits your interests and strengths best. It’s now time to focus on that and go as deep as you can. Constantly improve your skills and knowledge on the career path you choose. Set yourself apart as an expert in your chosen path. This will increase your value and compensation for your skills. 

How we Solve the Data Science Career Path Problem at Omdena

At Omdena we thought long and hard about how to find the best process for education in the technology and AI field. 

We believe in collaboration as the most effective way to learn, grow, and build a thriving career. In an Omdena project, you join a selected team of collaborators from diverse backgrounds. All members support each other in an open and safe environment where ideas are exchanged freely to find the best-fit solution(s) to a problem.

Depending on your interest and skill, you can follow one or more of 4 career development paths.

Key Takeaways 

Data science offers a range of lucrative careers that you can explore. Your focus should not be on becoming a data scientist, rather it should be on helping certain businesses solve certain problems. This means looking at your skills, interest areas, and career goals then identifying the best data science career path that will lead you there.