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Become a modern data engineer by learning cloud, Airflow, Spark, Data lake/warehouse, NoSQL, and real-time data pipelines

Become a data analyst through building hands-on data/business use cases

Become an AI/ML engineer by getting specialized in deep learning, computer vision, NLP, and MLOps

Become a DevOps Engineer by learning AWS, Docker, Kubernetes, IaaS, IaC (Terraform), and CI/CD

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Explore different Data Science career paths and how to get started

Data Scientist Career Path

Understanding the career path of a data scientist is important before you make a decision to transition your career. The good news is that data scientists have many career options. We’ve seen people going down different paths and be successful and happy with their jobs:

  • Technical track
  • Managerial track
  • Product
  • Consulting
  • Startup

If you’re passionate about technology and want to stay in the technical path, there are several options:

  • Become a senior or lead data scientist and work in different industries
  • Become more specialized in ML and turn into a machine learning engineer
  • Become a data engineer or even software engineer

Lead Data Scientist

Working as a lead data scientist requires not only technical skills. You will be setting the project roadmap along with the leaders, carrying out larger scope data projects, and lead junior data scientists to tackle challenging problems. If you want to become a lead data scientist, be prepared to:

  • Keep learning new techniques and have at least specialized area
  • Become a generalist since a large scope project will require more than just machine learning
  • Get comfortable working with different teams including: product, software, engineering, and business
  • Stay abreast of cutting-edge technologies and read more literatures in the AI field

The path for technical data scientist may look like this depending on the companies you work for:

  • Lead Data Scientist
  • Principal Data Scientist
  • Chief Data Scientist or Chief Scientist

Machine Learning Engineer

MLE is a specialized role. Going from a DS role to an MLE role requires stronger engineering skills. Machine Learning Engineers will spend more effort on dealing with big data, engineering ML pipelines, and work with MLOps to deployment models into production. If you want to become a ML engineer, try to learn cloud, docker, kubernetes, as well a Spark. Understanding of REST APIs and system design are useful too.

Data Engineer

We’ve seen many data scientists switching to data engineering in recent years. Some are going after a potentially higher compensation while others discovered better interest in engineering side of data projects.

Data Engineering requires less statistics, math, and machine learning. The requirements for coding is higher, data engineers need to be comfortable writing production-grade code. Data transformation functions need to be properly tested. And data engineers also need to have an architect-level view of the entire data pipeline and make sure things run smoothly in production.

Manager of Data Science

A data science manager’s role is similar to that of a lead data scientist. The difference is that Manager is a people manager role that involves managing the team, setting goals, as well as doing performance reviews.

Becoming a manager might mean that you will become less technical because you will allocate more time to work with your team of data scientists. You will be having regular one-on-one meetings to help them set goals, evaluate performance, as well as providing mentorship. That quickly eats up your time but you’re playing an important role in building a high-performance DS team.

Once you go down the managerial path, your options will be:

  • Senior Manager of Data Science
  • Director of Data Science or Head of Data Science
  • Chief Data Officer

Data Product Owner

Another interesting path to go down is the product manager route. Data-driven product managers are scarce resources. Startups building high-growth applications will want product managers who can work with a team of software engineers, data scientists and data engineers. Among those roles, data scientists usually work closely with the business teams and therefore it’s natural for some people-oriented data scientists to consider a role in product management. You’re more likely to see data scientists switching to product management in FANNG companies.

Consulting

With so many businesses going through digital transformations, the demand for data consultants become increasingly high. Many companies don’t have the budget to own or experience to run a data science team. But they still have interesting data problems. Companies that want to collect more data for advanced analytics also want some experts’ help on setting the data strategies. This is where consultants step in and provide lots of value.

If you like to work with business leaders and enjoying gaining data experience in various industries then consulting can be a great option for data scientists.

Check out other career guides

Our Career Guide provides all the resources you will need to help you get started in navigating data careers. We include free resources, guides, and tools to help you get started.

WeCloudData

WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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