Data scientists wrangle big data using deep analytical skills to create meaningful, actionable insights from unstructured data to solve problems and help organizations grow or achieve their goals. Consider exploring a data scientist career path if this sounds like a job you're cut out for.
There are different ways to pursue and progress through a career path of a data scientist, depending on the employing organization and industry. Key aspects to consider when pursuing a career path for a data scientist, like any role in data science, are the responsibilities, skills, salary, and growth opportunities.
In this article, we'll cover:
- What are the Different Data Scientist Career Paths?
- How to Become a Data Scientist — Career Path Timelines
- Data Science Career Path — In-Demand Skills and Experience Requirements
- Top Reasons Why a Data Scientist Career Path Is a Good Choice
- Data Scientist Career Path — Top Companies to Work For
- FAQs on Data Scientist Career Path
What Are the Different Data Scientist Career Paths?
A data scientist career path features two main career tracks:
- Individual Contributor Career Track
- Management Career Track
Both tracks require unique skills. You can attain these skills through upskilling and qualify for a high-level role if you don't possess these skills. If you want to test the waters before stepping into a managerial role, you can pursue a data scientist career path that involves a mix of people or leadership tasks and technical tasks within your team or project to gauge your interest levels in management.
Data Scientist Career Path — Individual Contributor (IC) Roles
In a data scientist career path as an individual contributor, data scientists work on core data science tasks like programming, creating models, coding, solving complex problems, and getting hands-on with the technical aspects of data science jobs.
IC data scientists can also provide technical leadership to project members. For example, a Staff Data Scientist can direct junior project members and be involved in project implementation and execution of tasks and activities without direct reports or people management responsibilities.
Advanced or deep technical or hard skills are key to developing an IC data scientist career path.
Typically, the data scientist career path for an individual contributor (IC) follows the following progression:
Data Scientist Career Path — Management Roles
A managerial role in a data scientist career path involves data scientists performing people management tasks such as leadership, building relationships, conflict resolution, etc.
Unlike software engineering managerial roles, where conceptual understanding of technologies used suffices to perform managerial tasks, data science managers need to have a working knowledge of technologies used. Data science managers don’t work on tasks like programming, coding, modeling, or executing technical aspects of projects. A data scientist career path in management involves:
- Meeting and talking to people
- Liaising between teams and upper management
- Coordinating and leading large projects
- Assigning responsibilities
- Delegating tasks
- Providing overall direction to data science projects
As managers, data scientists have direct reports and are responsible for mentorship and career development. Soft skills such as communication, leadership, collaboration, etc., are key to developing a data scientist career path in a management role.
Typically, the data scientist career path in management follows the following progression:
How to Become a Data Scientist — Career Path Timelines
You can pursue a data scientist career path in the following ways:
Earn the Right Educational Degrees or Certifications
Starting on your data scientist career path should begin with the right educational qualifications. In fact, most companies have established academic degrees as a prerequisite for a data scientist role. Depending on the stage of the data scientist career path you’re in, key educational degrees you will require are:
- Bachelor’s / Master’s Degree in Computer Science, Software Engineering, or a related field; Bachelor’s degree for an entry-level position and a Master’s degree for higher-level data scientist positions
- Ph.D. in a relevant field is preferable and often a prerequisite for advanced or research and development positions.
Alternatively, you can also obtain professional certifications in the required skills to pursue a career in data science. Some of the top data science certifications customized to enable software engineers and software developers to follow a data scientist career path are:
- Tensorflow Developer Certification
- Google Professional Data Engineer Certification
- Amazon AWS Big Data Certification
- Microsoft Certified Azure AI Fundamentals
- SAS Certified AI & Machine Learning Professional
- SAS Certified Big Data Professional
- SAS Certified Data Scientist
- Certified Analytics Professional (CAP)
- DASCA Senior Data Scientist (SDS)
- DASCA Principal Data Scientist (PDS)
Gain the Right Experience
Obtaining experience in data science fields is imperative to developing a successful data scientist career path. To follow a career path to become a data scientist, you will require:
- Data science experience
- Data analytics experience
- Software engineering experience
- Experience working with teams or in a collaborative environment
- Experience communicating with stakeholders
Experience required at different stages of a data scientist career path is outlined below:
Transitioning or Switching to a Data Scientist Career Path
It is possible to switch or transition from a software engineering career path to a data scientist career path. You can build on your software engineering experience:
- to make a lateral move or
- start at a lower level and gain the required skills and experience.
You can successfully transition into data science if you have the required skills and attributes to perform data science tasks.
The skillset required for a data scientist career path is broader than the typical software engineering skillset. For example, data scientists also possess advanced applied mathematics and statistical skills in addition to programming and coding skills.
If you want to move laterally from your current career path to a data scientist career path, you have to possess the minimum required qualifications specified for the desired data scientist role at your target company. One way to do this is by obtaining a bachelor’s or master’s degree in the relevant field or obtaining professional certifications.
However, if you feel you have the required computer science knowledge and experience but are not quite at the required level, then upskilling is the best way to close your skill gap and embark on a data scientist career path.
One of the most effective ways is to go via the interview prep route. Many engineers wishing to transition to a new domain are now undertaking technical interview preparation programs focused on building domain-specific technical skills and how to leverage them to crack interviews and land jobs in the new domain.
Data Science Career Path — In-Demand Skills and Experience Requirements
A common question posed before embarking on a data scientist career path is — ‘What are the skills and experience required to become a data science expert?’ We’ve listed below some of the top technical and soft skills needed for the role.
Technical Skills to Become a Data Scientist
- Data Mining
- Data Wrangling
- Machine Learning
- Artificial Intelligence
- Coding; Programming
- Python, R Java, C++, Scala
- SQL, Pig, Hive
- Predictive Modeling
- Data warehouse environments, relational databases, No SQL
- Math and Statistics — Linear Algebra, Bayes Theorem, Geometry, Multivariable Calculus, Probability, Discrete Math, Graph Theory
- Tableau, Excel, Microsoft Power BI, Plotly, Qlikview, Zoho Analytics, and other Business Intelligence and Data Visualization tools
- Hadoop, Apache Spark, Apache Kafka, TensorFlow, Pandas, Matplolib, Scikit-Learn, Spark MLib, Numpy, AWS Deep Learning AMI, and other data frameworks
Soft Skills to Become a Data Scientist
A successful data scientist career path is marked by great technical skills and must-have soft skills, as outlined below.
- Business Knowledge / Business Mindset
- Product Knowledge / Product Mindset
- Analytical Thinking
- Problem-solving
- Critical Thinking
- Skepticism
- Creativity
- Communication
- Collaboration
- Teamwork
- Storytelling
Top Reasons Why a Data Scientist Career Path Is a Good Choice
Data scientist career paths offer rewarding careers in data science in 2022, and this trend is expected to continue in the upcoming years.
- Data Scientists are in high demand: With ever-increasing digitization, growth of big data, and the impending widespread adoption of AI, ML, 5G, IoT, and the Metaverse, data scientists presently enjoy high demand across industries; a trend which is expected to continue in the coming years.
- Shortage of data scientists with specialized skills: Due to lack of talent, especially in ML and AI, companies are willing to pay premium salaries to rope in skilled data scientists.
- High salaries and earning potential: Data scientists are well compensated given that they enjoy high demand and are in low supply in the job market. Data scientist career path progressions are such that earnings potential increases multifold with experience. We delve further into data scientist salaries in the following sections.
- Long-term career path: Big data is only just taking off with an increasing number of businesses capitalizing on the competitive advantages data science has to offer. With the rise of new technologies, big data will be ever-expanding making the data scientist career path a long-term career option.
- Low risk of automation: Unlike many software engineering roles, data science relies on human intelligence, creativity, and specialized skills, reducing the risk of automation of data scientist roles.
Check out our Data Science interview prep course to learn how to develop skills to pursue a career path as a data scientist.
Data Scientist Career Path — Top Companies to Work For
FAANG+ companies are considered the best places to work in tech. Pursuing a data scientist career path at these companies is enviable given their leadership status, ability to pay high salaries, the opportunity to work on new or cutting edge technologies and projects, and their ability to draw the most talented engineering minds in the industry.
However, almost every industry deals with big data these days, and there is an ever-widening adoption of data science technologies in the market. Besides tech, the demand for data scientists is high in the following industries:
- Finance
- Professional Services
- Healthcare
- Retail
- Telecom
- Transportation
- Manufacturing
Citing a LinkedIn source, Datamation lists these companies with the highest number of job postings for Data Scientists:
Based on average salaries reported at each company, Indeed lists the following as top companies for data scientists in the United States:
Embarking on a data scientist career path can be easy once you’ve understood what it encompasses and how to navigate it, as detailed through this guide. In short, though, landing a data scientist role hinges on developing the required skills and knowledge to function effectively in the role of a data scientist.
FAQs on Data Scientist Career Path
Q1. What is the career path to becoming a data scientist?
There are two main ways to advance your data-centric career: become a data science professional, work as a data analyst, database developer, or data scientist, or move into an analytics role, such as a functional business analyst or a data-driven manager.
Q2. What is the difference between the career path for a data scientist and a data analyst?
Data analysts work with structured, existing data and solve specific business problems. In contrast, data scientists gather data, work on unstructured data, identify problems, analyze and model data, and interpret results for actionable insights. Data scientists require far more advanced skills than data analysts.
Q3. What is the difference between the career path of a data scientist and a data engineer?
Data scientists work on processing, analyzing, and interpreting unstructured data to unearth trends or patterns or other meaningful insights that can be utilized to improve business. Data engineers design and build the architecture, infrastructure, and systems that capture and generate data. They also build data pipelines.
Q4. What is the best specialization to pursue a data scientist career path?
Machine learning and artificial intelligence are two in-demand areas of specialization that data scientists can capitalize on to build a rewarding career path.
Q5. What is the next level of progression in a data scientist career path?
You can progress to senior technical roles as an individual contributor or senior management roles. Alternatively, you can build on your skills as a data scientist and pursue a machine learning engineer career path.
Q6. Is data scientist a good career option?
Demand for data science is projected to grow in the years to come, making data scientist career paths rewarding and stable career options.
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