What Does a Data Science Career Path Look Like?

Data science is a booming field, and the demand for qualified data scientists is high. Job openings for data science professionals has grown by 480% for the occupation since 2016 when there were 1,700 data scientist job openings. As of date, Glassdoor reports over 10,000 open positions for data science professionals with an average salary of $120,000.If you have an analytical mind and enjoy working with numbers, here’s what a data science career path looks like.

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Data Analyst

Data analysts help decision-makers have a deeper understanding of their market, industry trends and company impact. Most importantly this work enables, leaders to make better-informed decisions. ]

Data analysts are expert communicators. In addition to knowing how to manipulate data that is curated by data engineers and cleaned and organized by data scientists, they are comfortable taking these big complicated models and concepts and distilling them into high-quality insights for non-technical stakeholders. 

They are comfortable working with metrics like click-through rates on a site or conversion rates on a landing page. These data professionals review data to identify trends and patterns, work with data scientists to test algorithms, and consult with engineers when data needs to be restructured to optimize the speed and efficiency of the company’s systems. They then organize the data into reports and dashboards to make it easier for other employees to access and analyze. Reporting dashboards are often visual and interactive, making data easier to understand. 

Finally, they meet with teams to consult on the best ways to utilize data to improve their workflows. In addition to communication skills, data analysts need to have knowledge of various analytical techniques as well as data visualization tools. 

Skills Needed To Succeed as a Data Analyst

Data analysts need to be able to work with a wide variety of data, from structured data in databases to unstructured data in documents and spreadsheets. Specific skills required include the following: 

Data analysts need to be able to work with a wide variety of data, from structured data in databases to unstructured data in documents and spreadsheets. Specific skills required include the following:  

  • Data Analysis techniques (regression, time series and predictive)
  • Advanced math such as linear algebra, calculus and statistics
  • Knowledge of programming languages such as Python and R
  • Data extraction, transformation and loading techniques
  • Data visualization tools such as Tableau or Power BI to create dashboards
  • Machine learning

An important non-technical skill is communication. Data analysts must be effective at presenting complex information in an easily-digestible format. This makes it easy for business leaders to get the insight they need for decision-making.

Data Engineer

Data engineers design and maintain a company’s data architecture. This can involve anything from creating a new data warehouse to creating a data lake. Data engineers often work with big data technologies, such as Hadoop or Spark, to process large volumes of data. 

They also architect and maintain data pipelines that move data from one location to another. Engineers analyze data flows and data usage patterns to identify ways of improving data efficiency and speed. 

Because of their role as data professionals, data engineers are at the forefront of the data science process and are often the first point of contact between data scientists and the information a scientist needs. They communicate with scientists to understand the project requirements and the data needed. 

Once the data engineer identifies the appropriate data, they use a variety of techniques to transform the information into a standardized format that is easier to work with and analyze. 

Skills Needed To Succeed as a Data Engineer
  • Engineers must be comfortable with a variety of data sources, from structured data in databases to unstructured data. 
  • Data engineers should have strong analytical skills and the ability to think creatively about how to solve problems. 
  • They should be able to work well in teams and communicate effectively with other members of the team. 
  • Data engineers need to know a variety of programming languages and platforms and a wide range of visualization tools. Including 
    • Python 
    • Scala 
    • Spark 
    • Hadoop 
    • Hive 
    • Cloud computing (such as with AWS)
    • Apache Airflow
    • Bash
Salary and Job Outlook

The job outlook for data engineers is expected to be quite strong, and data engineers can expect to see a high demand for their skills in the years to come. Data engineers make an average of $100k per year.

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Data Scientist

Many people confuse the data analyst and data scientist roles. Namely, because they are used interchangeably. However, these are two distinct fields. 

The main role of a data scientist is to generate actionable insights from data. Data scientists explore and analyze large amounts of data to reveal patterns, trends, and insights. 

This may involve exploring data to find out if the model used to predict outcomes is working as expected or identifying ways to improve the model based on existing data. They collaborate with engineers to create algorithms that process unstructured data such as text from emails or online forums.

Skills Needed To Succeed as a Data Scientist

Data scientists must be able to work with both structured and unstructured data to generate actionable insights. They often need a bachelor’s degree in computer science, mathematics or a related field. Specific skills a scientist’s needs include:

  • Data analysis techniques
  • Database management
  • Machine Learning
  • Natural Language Processing
  • Mathematics 
  • Data visualization
  • The ability to experiment with new technologies to find the right fit
Salary and Job Outlook

The job outlook for data scientists is promising, as the U.S. Bureau of Labor Statistics predicts that this field will grow much faster than average, at 22% from 2016 to 2026. What’s more, the Bureau predicts that there will be a shortage of data scientists, especially those with advanced degrees. Those in this role can expect to make an average of $104k per year.

Individual Contributor Career Track vs. Management Career Track

As you progress in your career, you might start to think about the different paths you could potentially take to further your career. There are many different roles and job titles at every company, but most jobs can be placed into one of two categories: individual contributor or management.

Individual contributors are people who work autonomously and have little to no responsibility for the work of others. On the other hand, management is responsible for helping teams achieve their goals.

Individual Contributor Roles in Data Science:
  • Data analyst
  • Data engineer
  • Data scientist
  • Business intelligence analyst
  • Business intelligence developer
Management Roles in Data Science:
  • Chief Data Officer: A chief data officer (CDO) is a senior executive who is responsible for the company’s data strategy and transformation. Their job is to ensure that the company collects and processes data in a way that will provide the most value to the business and stakeholders. In addition to setting the data strategy, the CDO must work with the company’s stakeholders to understand how they currently use data so they can set goals for how they want to use data in the future. Because the CDO is responsible for the company’s data, they must hire a team that can keep the company’s data secure.
  • Chief Information Officer: A company’s chief information officer (CIO) is responsible for keeping the company’s technology up-to-date and secure. This role often requires advanced degrees in computer science or engineering and a proven track record in a technical field. Most companies hiring for this position require their candidates to have at least a bachelor’s degree. A CIO also typically has several years of experience in the technology industry.
  • Chief Analytics Officer: A company’s most important data asset is its customers. A Chief Analytics Officer (CAO) is a person who is responsible for making sure that the company has the right data to make informed decisions about its customers. The CAO’s job is to make sure that the company is collecting data from its customers as well as other stakeholders (such as suppliers) and that it is feeding that data into the right analytic tools.
  • Chief data scientist: A chief data scientist is a senior-level data expert responsible for developing an organization’s data-driven transformation strategy. Chief data scientists are responsible for collecting data, analyzing data, and visualizing data to inform business decisions. They also offer advice on how to use data to solve business problems. As a high-level data expert, a chief data scientist is expected to have deep knowledge of the various data sets that are collected by different departments within the organization.

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