Data Science Career Paths: Comparing Data Scientists, Data Engineers, and Statisticians

The field of data science has exploded in recent years, driven in large part by advances in artificial intelligence (AI) and machine learning (ML). As companies scramble to hire data experts who can help them harness the power of AI/ML, a plethora of new career paths have emerged. Among the most prominent are data scientists, data engineers, and statisticians.

While there is significant overlap between these roles, each has a distinct focus area. In this article, we‘ll dive deep into the nuances of each role, with a special emphasis on comparing data analysts and statisticians. We‘ll also explore how the rise of AI/ML is transforming these careers and what it takes to be successful in this dynamic field.

The AI/ML-Driven Boom in Data Science

The global market for AI is expected to soar to $190 billion by 2025, according to research firm Markets and Markets. A key driver of this growth is the increasing adoption of AI/ML across industries. From healthcare and finance to retail and manufacturing, companies are leveraging AI/ML to automate processes, improve decision making, and create innovative products and services.

This AI/ML boom has created a seemingly insatiable demand for data experts. A 2019 LinkedIn report found that data scientist roles have grown more than 650% since 2012, making it the fastest growing job in the U.S.

But data science is a broad field, encompassing many different specialties and skill sets. Let‘s take a closer look at three of the most common data science roles – data scientists, data engineers, and statisticians – and how AI/ML is impacting each one.

Data Scientists: The Swiss Army Knives of Data

Data scientists are often described as the "Swiss Army knives" of data. They have a versatile skill set that allows them to work on all stages of the data science process, from data collection and processing to analysis, visualization, and communication of results.

According to a 2020 Kaggle survey of over 20,000 data professionals, the most common skills of data scientists include:

• Python (used by 87% of data scientists)
• SQL (55%)
• Machine learning algorithms (53%)
• Data visualization (52%)
• Statistics (48%)

In the era of AI/ML, data scientists increasingly need expertise in techniques like deep learning, neural networks, and natural language processing (NLP). They also need to be skilled in working with big data technologies like Hadoop and Spark, and cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

The role of AI/ML in a data scientist‘s job is perhaps best summed up by Cassie Kozyrkov, Chief Decision Scientist at Google, who describes it as:

"Applied machine learning happens at the intersection of data science and AI engineering. In any AI project, there‘s a data-focused scientist who translates a business problem into a machine learning task, prepares the right data, and interprets model results."

Data Engineers: The Architects of Data Infrastructure

If data scientists are the Swiss Army knives of data, then data engineers are the architects. They design, build, and maintain the data infrastructure that makes data science possible. This includes everything from data pipelines and databases to data lakes and warehouses.

The key responsibilities of data engineers include:

• Developing and maintaining data pipelines for ingesting, transforming, and loading data
• Building and optimizing data storage systems
• Ensuring data reliability, efficiency, and security
• Collaborating with data scientists to understand data needs and provide the right data at the right time

Like data scientists, data engineers increasingly work with AI/ML technologies. For example, they may use ML to automate data quality checks, detect anomalies in data streams, or optimize database queries. They also need expertise in big data tools like Hadoop, Spark, and NoSQL databases.

The rising prominence of AI is reshaping the data engineer‘s role. As Mihail Eric, CTO of data engineering platform Databand, explains:

"As companies turn to AI to offer innovative new products, automate business processes and enhance decision-making, there is explosive growth in the demand for the high-quality data that powers AI systems. Data engineers are at the center of this transformation, responsible for building the data platforms that enable the AI revolution."

Statisticians: The Math Wizards of Data Science

Statisticians are the math experts of the data science world. They use mathematical and statistical techniques to collect, analyze, and interpret data. Traditionally, statisticians have worked in fields like healthcare, finance, and government, but the rise of data science has created new opportunities in tech and other industries.

The core skills of statisticians include:

• Probability and statistical theory
• Experimental design
• Sampling and survey techniques
• Regression analysis
• Statistical programming (e.g. R, SAS, SPSS)

While AI/ML is transforming many aspects of data science, the fundamental math skills of statisticians remain highly relevant. In fact, many of the cutting-edge techniques in AI/ML, such as Bayesian inference and stochastic optimization, have roots in statistical theory.

However, modern statisticians increasingly need to complement their math skills with expertise in programming, data wrangling, and visualization. As renowned statistician Hadley Wickham argues, "to be an effective statistician today, you need to be able to write code."

Many statisticians are also expanding into more ML-focused roles. A 2019 American Statistical Association survey found that over half of statisticians now use ML in their work.

Data Analysts vs Statisticians: A Tale of Two Skill Sets

Among the various data science roles, data analysts and statisticians are perhaps the most often confused. While there is significant overlap between them, there are also key differences in their backgrounds, skill sets, and day-to-day work. Let‘s dive deeper into the comparison.

Educational Background

• Data analysts often have a bachelor‘s degree in a quantitative field like statistics, mathematics, economics, or computer science. Some also have a master‘s degree in analytics or a related field. Coursework typically includes statistics, programming, databases, and data visualization.

• Statisticians usually have at least a bachelor‘s or master‘s degree in statistics or mathematics. Many also pursue a PhD, especially those working in academia or research roles. Coursework is heavily focused on probability, statistical theory, and mathematical modeling.

However, with the rise of AI/ML, a growing number of universities are now offering specialized degrees in data science and artificial intelligence. For example, Carnegie Mellon University offers a Master of Computational Data Science degree focused on ML and big data, while University of Michigan offers a Master of Applied Data Science degree that includes coursework in deep learning and NLP.

Core Skills and Tools

Data analysts need a diverse set of skills encompassing:

• Querying databases using SQL
• Data wrangling and cleaning
• Exploratory data analysis
• Building dashboards and visualizations
• Programming in languages like R and Python

The most common tools used by data analysts include Excel, SQL databases, Tableau, PowerBI, and scripting languages like R and Python.

Statisticians, on the other hand, need deep expertise in:

• Probability distributions
• Statistical modeling and inference
• Experimental design
• Sampling techniques
• Time series analysis

Statisticians typically work with tools like SAS, SPSS, R, and Stata. However, Python is also growing in popularity due to its rich ecosystem of statistical and ML libraries.

With the growing ubiquity of AI/ML, both data analysts and statisticians are adding ML skills to their toolbox. According to the Kaggle survey, 41% of data analysts and 30% of statisticians now use ML algorithms and techniques in their work.

Day-to-Day Work

A typical day for a data analyst might include:

• Querying a database to pull a specific dataset
• Cleaning and preprocessing data to handle missing values and outliers
• Performing exploratory analysis to identify trends and patterns
• Creating a dashboard in Tableau to visualize key metrics
• Meeting with business stakeholders to discuss findings and recommendations

Statisticians, in contrast, might spend their day:

• Designing an experiment to test the effectiveness of a new drug
• Developing a statistical model to predict customer churn
• Analyzing survey data to infer population characteristics
• Writing R code to simulate data from a complex probability distribution
• Preparing a technical report detailing methodology and results

The key difference is that data analysts tend to work more closely with business stakeholders and focus on delivering insights that inform business decisions, while statisticians tend to work on more research-oriented tasks and deliver detailed technical documentation of their work.

Industry and Salary Trends

Both data analysts and statisticians are in high demand across industries, but there are some notable differences in job market trends:

• Data analysts have particularly strong opportunities in tech, marketing, and finance. According to Glassdoor, the average base salary for a data analyst is $67,377 per year, with higher salaries in tech hubs like San Francisco ($90,958) and New York ($78,909).

• Statisticians have ample opportunities in healthcare, biotech, government, and academia. The Bureau of Labor Statistics reports a median salary of $92,600 per year, with the top 10% earning over $146,770.

However, as AI/ML reshapes industries, the lines between these roles will blur. LinkedIn‘s 2020 Emerging Jobs Report predicts growing demand for "AI Specialists" across industries, creating new opportunities at the intersection of data analysis and ML.

Charting Your Path in an AI-Driven Data World

With the rapid rise of AI/ML, the field of data science is evolving at breakneck speed. Aspiring data professionals have a dizzying array of career paths to choose from, each requiring a distinct mix of skills and experience. So how do you choose between becoming a data scientist, data engineer, data analyst, or statistician?

Here are some key factors to consider:

• Educational background: If you have a degree in statistics or math, the statistician path may be a natural fit. A computer science or general quantitative degree aligns well with data analyst and data engineer roles. A more specialized data science or ML degree can set you up for data scientist roles.

• Skills and interests: Do you geek out over probability distributions and statistical theory? Statistician may be your calling. Love programming and building data pipelines? Consider data engineering. Enjoy working with messy real-world data to tease out business insights? Data analyst could be your sweet spot.

• Career goals: If your dream is to work on cutting-edge AI/ML projects in Silicon Valley, then pursuing data scientist or ML engineer roles at big tech companies may be the way to go. If you‘re passionate about health and biotech, then statistician or data analyst roles could be a great fit. Fascinated by data infrastructure? Then data engineer could be your ideal path.

Whichever path you choose, one thing is certain – an exciting and impactful career awaits in the world of data science. As Cassie Kozyrkov sums it up:

"Data is the new oil, and data science is the refinery. As an aspiring data expert in an AI-driven world, you have the opportunity to be at the forefront of extracting value from this new precious resource. The key is to never stop learning, stay curious, and be adaptable as the field continues to evolve at a rapid pace."