Building a Data Science Career Path

Table of Contents

• [toc headings="h2" title="Table of Contents"] People following the data science job market over the past few years might be feeling some whiplash. The hiring boom of 2020 was followed by a flood of layoffs in 2022 and 2023, leaving some to question the future of the data science career path.  The good news is that this erratic job market has stabilized over the past year. In the process, the number of data science job postings grew by 130% from July 2023 through July 2024. Openings for adjacent jobs like data analysis roles and business intelligence careers were also on the rise, while layoffs decreased by 15.3% over that same span of time. The takeaway for job seekers from these data science industry trends is that there are still ample opportunities in this sector for both experienced professionals and newcomers to the field. If you’re considering a career in data science, here is some information and advice to help you get started.

• Understanding the data scientist role

• Data scientists help organizations make better decisions by analyzing and interpreting complex data. This requires a combination of expertise in statistics, programming, and the specific industries or domains where they’re providing insights.  The day-to-day work of a data scientist will vary depending on their job level and the size and type of organization where they’re employed. Often, they’re responsible for collecting data from various sources then processing and cleaning it to ensure its quality and accuracy. From there, they explore trends and patterns in that data, which they use to build predictive models using machine learning algorithms, or help organizations optimize their process and make better data-driven decisions. A data scientist’s ability to turn raw information into actionable insights is valuable across industries in today’s business landscape. In a survey of open jobs from 365 Data Science, the technology & engineering sectors still led the pack with just over a quarter (28.2%) of the current openings. That said, data science is gaining popularity in sectors like HR (19%), health & life sciences (13.1%), and financial & professional service (10.3%), as well as in areas like manufacturing, transportation, commerce, and real estate.

• Key skills for data scientists

• Across industries and specializations, there are some core data scientist skills that professionals need to have. Here are the most consistently in-demand skill sets that employers look for when they’re hiring into these roles.

• Programming skills

• Raw data is often messy and incomplete. Programming skills allow data scientists to clean and organize this data more efficiently, with a lower risk of errors than if it’s prepared manually. Python and R are considered the essential programming languages for data scientists. These languages provide libraries like Pandas, NumPy, and scikit-learn that handle tasks like filtering, aggregating, and reshaping data, or creating predictive models that can be customized to solve specific problems.   It’s also common for data scientists to handle large datasets that exceed the capabilities of traditional spreadsheets. Programming lets them work with distributed computing frameworks like Hadoop or Spark that efficiently process these large volumes of data.

• Mathematics and statistics

• Much of a data scientist’s role comes down to analyzing past data to extrapolate what’s likely to happen in the future. To do that means understanding statistical concepts like mean, median, variance, and standard deviation, while knowledge of probability theory helps them assess model uncertainty and quantify risks so they can make informed decisions based on data. Often, data scientists don’t stop at making a prediction based on past events. They also use statistics to test their theories and validate their predictive models. This requires knowledge of probability distributions and skills in statistical hypothesis testing to determine whether the patterns they’ve observed are meaningful or the result of chance. By using hypothesis testing and similar statistical methods, data scientists can go beyond speculation and deliver evidence-based insights. One common example of this is A/B testing, where two versions of a product or strategy are tried to assess which performs better. Approaches like this help data scientists validate their models and increase their confidence in the insights they deliver.

• Data visualization

• One of the reasons organizations need data scientists in the first place is that large, complex data sets can be hard to understand in their raw form. This is where data visualization expertise comes into play. Using tools like Tableau, Power BI, and Seaborn let data scientists create graphs, charts, and other visual representations of data that can help business leaders and other decision makers understand the patterns, trends, and insights it offers. This enables data scientists to effectively communicate their findings and tell the story buried within the raw data they work with.

• Machine learning and AI

• Machine learning jobs are one of the fastest growing segments of the data science employment market. Even in roles that aren’t explicitly categorized AI career opportunities, understanding how these algorithms function enables data scientists to do their jobs more effectively. Machine learning algorithms are commonly used for tasks like regression for predicting values, or clustering to group data points. Deep learning neural networks and frameworks like PyTorch and TensorFlow help data scientists solve advanced problems using large data sets.

• Education for data scientists

• According to data from Zippia, just over half (50.7%) of data scientists have a bachelor’s degree, while 34% hold  Master’s and 13% have a doctorate. This is congruent with 365 Data Science’s findings that 47.4% of job postings for data engineering roles in 2024 required a degree, often an advanced degree when you’re looking at senior data scientist roles. While some kind of academic credential is often necessary to start a data science career, these professionals come from a variety of backgrounds. Computer Science is a popular major since it provides a strong foundation in programming, databases, algorithms, and other essential skills. Statistics is another popular major choice, giving students an in-depth understanding of concepts like probability theory, hypothesis testing, and statistical inference. Applied Mathematics is a similar course of study that can provide this foundational knowledge. Other data scientists focus on a specific domain, like Engineering, Economics, Business, or Management Information Systems. As data science has grown as a profession, more universities have started to offer dedicated degrees in the field. These programs include courses from all of the areas mentioned above, providing education tailored to the specific needs of big data careers. If you don’t yet have a 4-year degree, obtaining one is likely your best first step to getting started in the data science profession. That’s not the only way to acquire the specific skills you’ll need for these roles, though, especially for those who are switching into data science from other technical fields like IT or software development. There are two main alternative education paths that will get you to a job faster than enrolling in another college program: bootcamps and certifications.

• Data science bootcamps

• A data science bootcamp is an intensive course that teaches students a lot of the same skills they’d learn in a degree program but in a fraction of the time. These programs are more targeted than degree programs, too, with a laser focus on the specific knowledge and skills data scientists will need in the workplace. The tighter focus and shorter timeframe of a bootcamp are its main advantages. These programs can last anywhere from a few weeks to several months, but in any case are much shorter than the four years (or longer) it takes to earn a degree. There’s another benefit to this faster timeframe, too, which is that these programs cost significantly less than a four-year degree. Now, many employers of data scientists do look for candidates who have a college degree—but that degree doesn’t necessarily need to be in this specific domain. For someone who already has a degree in another field, going through a bootcamp can be an effective way to gain the specific skills you’ll need and start to build your data science project portfolio, at a fraction of the time and cost of obtaining another college degree. There are a variety of bootcamp programs out there. While many of them will cover the same basic topics, this doesn’t mean they’re all equally useful for every professional. Some are focused more on data science fundamentals for entry-level roles, for instance, while others focus on a particular specialization, or are designed for professionals who want to prepare for senior or leadership roles. Because of this, the first step to choosing the right bootcamp is to clarify your career goals and the specific requirements of the jobs you most want to land. You should also assess your current skill sets and education to identify the gaps you need a bootcamp program to fill in. For instance, if you already have a degree in an area like software development, you likely are already comfortable with the programming side of the role. A bootcamp that focuses more on concepts like machine learning, data visualization, or big data frameworks is likely to provide you with more value as a result.

• Popular data science certifications

• Many bootcamp programs culminate in a professional certification. This is a formal credential obtained through a professional organization or educational institution after completing a course or program covering the key topics verified by the credential. To officially earn the certificate usually means passing an exam, and often professionals who already have the required skills can take the exam without having completed any specific courses. Going through a bootcamp isn’t the only way to obtain a data science certification. Some certifications do have prerequisites, such as a certain number of years of experience in the field or a lower-level certification, but very often anyone who wants to develop and prove their data science skills can enroll in the course and certification exam. Just like with bootcamps, there are several data science certifications that you can earn. These include vendor-neutral certifications, which cover general skills related to the data science profession, as well as courses focused on a specific platform that is widely used in the profession. Examples of this latter type include certifications like Microsoft Azure AI Fundamentals or the TensorFlow Developer Certificate. As a general rule, when you’re just starting off in your career, a vendor-neutral certification is going to be the best option. This gives you versatile skills that most or all organizations hiring data scientists will look for. Some of the most sought after data science certifications to obtain as a new data science professional include:

  • Associate Certified Analytics Professional (aCAP) – This is the entry level equivalent of the prestigious CAP certification and covers the 7 domains of analytics, including business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management.
  • SAS Certified Advanced Analytics Professional – This credential verifies professionals’ ability to use statistical analysis and predictive modeling to analyze big data. Obtaining it requires passing three separate exams, making it a very comprehensive credential. There are also other certifications through SAS of value to data scientists, such as their general Data Science Certification and Machine Learning Specialist credential.
  • IBM Data Science Professional Certificate – This certification includes 9 courses covering topics including data science methodology, data visualization, machine learning, databases and SQL skills for data scientists, and Python. It’s free through Coursera and takes three months to complete on average.

• Early career options

• Once you’ve completed your education, you’re ready to start your search for entry-level data science jobs. At this level, roles that lead to future careers in data science often have a slightly different name, such as Data Analyst or Data Engineer. These tend to be more generalist roles that deal hands-on with the curation, cleaning, and analysis of data.  One option for breaking into the data science career path is to find a position as a data science intern. While internships are often seen as positions for students, this doesn’t mean you need to be enrolled in a degree program to land one. These are paid positions, too, and in fact can come with a very competitive salary, so you don’t need to sacrifice your earning potential to take this kind of position. Interns work with more experienced professionals, helping them with their work while they learn the fundamentals of the position. Their typical day-to-day work includes things like cleaning and preparing data for analysis or developing new machine learning models and visualizations, which is the same kind of work you’ll do in other data science roles. Aside from internships, there are some other truly entry-level data science positions you’re likely to find. Let’s take a closer look at a few of the most widely available options.

• Junior data scientist

• Average salary: $88,000 per year Junior data scientists do the same basic tasks as their more senior counterparts: analyzing data and communicating the results of that analysis to other team members. The key difference is that they tend to be under a more senior team member who is steering the project, meaning they have less decision making power. They also are less likely to work with complex models or large datasets. Skills in Python and SQL are helpful in this role, as is basic knowledge of business analytics.

• Junior data engineer

• Average salary: $72,000 per year Data engineer roles tend to be highly technical and focused on working with data, from the collection through the analysis stage. They often work on smaller projects and have less responsibility than their senior counterparts. A strong background in statistics and data visualization is helpful in these positions, as is knowledge of SQL, Python, and machine learning.

• Junior data analyst

• Average salary: $57,000 per year Data analysts in general focus on managing and analyzing large data sets. In a junior role, they typically work with more experienced analytics managers, helping them with tasks like cleaning data. These professionals need a sharp eye for detail, along with firm understanding of concepts like probability and statistics and experience programming in languages like Python, R, and SAS/SPSS.

• Junior data modeler

• Average salary: $103,000 per year These are entry-level professionals who help to visualize data by creating tables, charts, graphs, and other models that allow others to derive meaningful insights. They may also be responsible for other tasks related to data modeling, like designing triggers and indexes. To perform these tasks, data modelers need a background in relational databases and writing queries, and should be comfortable with a variety of platforms like SQL and Excel.

• Junior database administrator

• Average salary: $72,000 per year Database administrators manage database-driven applications. In a junior role, they tend to focus on the day-to-day operation of the database. This can include tasks like monitoring the database performance and troubleshooting issues. Understanding how to write queries in various languages is a key skill, as is using database management tools.

• Career growth in data science

• As you gain experience through an entry-level role, one way to advance along your career path is to move up into more senior versions of those same positions mentioned above. As you might expect, data science salary expectations go up as you accrue more experience and chop the “junior” off the front of your title (or even replace it with a “senior”). Higher positions will also typically be assigned more complex projects and may take on additional responsibilities, such as leading teams or mentoring junior employees. The truth is, there are multiple ways for data scientists to advance their careers. Some professionals advance by deepening their expertise in a technical specialization, like machine learning or data engineering. Others gain specialization in the business side of the domain, taking on roles where they can serve as a bridge between data teams and decision makers, or become leaders themselves in management or executive roles. Let’s take a closer look at these options, starting with some common data science specializations then transitioning into leadership roles data scientists can aspire toward.

• Specializations in data science

• Data science is a versatile field, which translates to a lot of ways that these professionals can hone in on a specialization. Here are some of the most in-demand and highest-paying specializations in the data science field.

• Machine learning

• Average salary: $160,000 per year Machine learning engineers build models and develop algorithms to power AI-driven services, enabling machines to make predictions and learn based on data they’re given. Subsets of this specialization include deep learning, which involves neural networks and is used for fields like natural language processing, autonomous driving, and image recognition. Machine learning experts work in industries ranging from tech companies to healthcare and finance organizations.

• Computer vision

• Average salary: $122,000 per year An expert in computer vision excels at extracting data from videos and images. This capability is highly sought after in fields like facial recognition, augmented reality, medical imaging, and autonomous driving, where it’s used for object avoidance. Proficiency in machine learning libraries is useful for this specialization, as well, as are strong analytical and creative problem-solving skills.

• Business intelligence

• Average salary: $113,000 per year These are statistical analysis professions that support business decision-making through data visualization and reporting. Business intelligence developers frequently use tools like Tableau and Power BI to deliver insights and need to have strong business acumen in addition to skills in data analysis and visualization.

• Big data

• Average salary: $97,000 per year Specialists in big data are experts in working with large datasets that require distributed computing tools like Spark, Hadoop, and NoSQL. These are critical positions in companies that make use of large-scale data analytics, which are found in industries including tech, cloud computing, and finance. Key skill sets include proficiency in programming languages like R and Python, as well as expertise in databases, data mining, data visualization, and machine learning.

• Leadership positions in data science

• While many data scientists prefer to focus on the technical side of the domain, there are also a wide variety of leadership positions available as they advance in their career. Here are some of the job titles leaders in this field can aspire toward.

• Data science manager

• Average salary: $165,000 per year Data science managers oversee teams of data scientists, managing their projects and ensuring the solutions they produce align with the organization’s goals. They often serve as a liaison between technical teams and decision makers, requiring strong communication skills and the ability to express technical terms to non-technical audiences in addition to a deep understanding of data science techniques.

• Data engineering lead

• Average salary: $142,000 per year Data engineers oversee the collection, storage, and accessibility of data, responsible for building the technical infrastructure used by data scientists. As a data engineering lead or head of data engineering, professionals manage the data engineering team and architecture for reliability and performance. This is an ideal position for data scientists who want to take on a leadership role but still have an active role in the technical side of the profession. 

• Head of data science

• Average salary: $165,000 per year This position oversees the entire data science function within an organization. Their focus is typically on the long-term impact and use of data science across departments, and they often seek out ways to scale the data science function and integrate it throughout the business. They also develop and implement the overarching data strategy, aligning the data vision with business goals and overseeing the data’s governance and quality standards.

• Analytics director

• Average salary: $179,000 per year The Director of Analytics role is similar to Head of Data Science but with a broader scope, overseeing the use of data analytics overall across an organization. They manage teams that handle every stage of this process, from data collection and cleaning through analysis and translation of that data into actionable insights. This requires expertise in business intelligence and strategic thinking, along with deep knowledge of data analytics.

• Chief Data Officer (CDO)

• Average salary: $205,000 per year Data science professionals increasingly have a seat at the executive leadership table through roles like Chief Data Officer. The CDO oversees data as a corporate asset, developing and leading data initiatives and the big-picture data strategy. They tend to have expertise in data privacy and governance as well as business strategy and high-level leadership skills.

• Building your data science career

• One thing that becomes clear as you learn about data science professions is that there’s no one way to build a career in this domain. Those who excel at deriving meaningful insights from data are in high demand across industries, and that gives them a wide variety of job options to choose from. You can use the information in this article as a starting point as you home in on your ideal data science career path.