Typical Day of a Data Scientist

| ComputerScience.org Staff

Typical Day of a Data Scientist
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Data science has become an important tech industry niche in the age of big data. Glassdoor's review of the 50 best jobs for 2022 ranked data scientists in third place based on median salary, job satisfaction, and number of job openings.

The discipline combines statistical analysis with software engineering. Successful data scientists excel at asking strategic questions to extract valuable insights from large data sets.

This page explores a typical day in the life of a data scientist and explains the various settings where these professionals work.

What Is a Data Scientist?

A typical day for a data scientist revolves around analyzing data to find useful insights. Primary responsibilities include:

  • Identifying organizational issues that data analytics might help solve
  • Collecting, verifying, and validating unstructured and structured data
  • Applying mathematical models and computer algorithms to validated data sets

Data scientists need strong skills in both statistics and software engineering. These specialists mainly work in the private sector. They may collaborate with other technology professionals including software engineers and information research scientists.

What Does a Data Scientist Do?

Data-based insights have revolutionized many industries while creating new avenues for profit. To realize these benefits, companies must look at the right data sets and ask the right questions. The specialized training that data scientists undergo develops advanced skills in these areas.

Knowing which data to look at and which questions to ask is one of the data science profession's major challenges. Professionals with strong aptitudes for statistical analysis, logical and mathematical thinking, creativity, and technology tend to succeed in the role.

The following section analyzes major job duties in more detail.

Data Scientist Responsibilities

  • Identifying Organizational Challenges: Organizations face internal and external challenges in areas like employee productivity and pressure from competitors. Advanced data analysis can often identify patterns that indicate potential solutions. Data scientists identify organizational challenges that targeted statistical analysis can address.
  • Assembling Data: First, data scientists identify an organizational issue to explore through data analysis. Next, they collect relevant statistical information. This process involves harvesting data, usually from many sources. Data scientists then check the collected information for completeness and accuracy.
  • Analyzing Data: Data scientists sometimes analyze data by hand. However, in most cases they use software-based tools such as algorithms to filter data strategically. Data scientists may take part in developing these algorithms. They also devise the modeling methods used to generate data-based insights.
  • Extracting Insights: Data scientists apply algorithms, mathematical models, and filters to large data sets. Then, they analyze the resulting statistics to extract actionable insights.
  • Communicating Findings: Capable data scientists often uncover observations worth reporting to organizational decision-makers. They communicate these findings in writing, with charts and graphs, or through a combination of both. This aspect of the job may also involve giving oral presentations or reports.

A Typical Day for a Data Scientist

Can you describe a data scientist's typical day at work?

In my experience, it's hard to say what even counts as a "typical" day. I have had the opportunity to work on a wide variety of projects at NNData, but even within projects the day-to-day can vary greatly. Some days are spent coding, some spent testing/debugging, some spent researching new/different approaches to problems, some on coordinating project deliverables with other teams. Generally, days will be mixtures of those things (and more). A data science team (and even each individual data scientist) should always have a plan of action for a day or a week, but be prepared for that plan to be disrupted by unexpected problems, changes in client desires, or any of a thousand other things that can change priorities. It is important for a data scientist to think and plan dynamically.

What skills do you find yourself using the most in your day-to-day job?

There are two skills that I use daily: mathematics and communication. Programming skills are good, data engineering skills and knowledge help me to coordinate projects more effectively, but the basic skills of mathematics and communication are even more important on a near-constant basis. A data scientist needs to have a strong understanding of mathematics and statistics to understand what models and methods are appropriate for a given problem, quickly master new models and methods, determine what machine learning techniques are best suited to any task, and know whether machine learning is appropriate for a task at all. Perhaps more importantly, mathematical reasoning can help a data scientist to interpret results from any analytics they apply and understand how results could have come from a data set. Communication skills are vital to sharing and expanding ideas with team members, managers, and clients. Data analytics and models are only useful if you can communicate why they are useful and what actionable insight can be gained from their results.

Can you describe your workplace? Do you work in an office, from home, or in a hybrid environment?

My work is entirely remote, and I think that is an excellent model for data science. It is much easier to be productive on the job and live a fulfilling personal life without the need for a commute. Also, not having to move to a new city with a new job is fantastic. For data scientists, there is really no aspect of work that can't be done remotely just as effectively as in person, if not more so.

A data scientist needs to have a strong understanding of mathematics and statistics to understand what models and methods are appropriate for a given problem, quickly master new models and methods, determine what machine learning techniques are best suited to any task, and know whether machine learning is appropriate for a task at all.

Is there a lot of collaboration in your role, or is it mostly independent work?

My role has a mixture of collaboration and independent work. Over the last year and a half, I have been involved with half a dozen projects, some of which required more independent problem solving, and some were more explicitly team-oriented. The team with which I work at NNData is very supportive; no matter the project or situation, the team can pool our resources to help come up with solutions to problems that might be creating roadblocks for any one of us, whether it be an elusive bug in a code, or difficulty implementing a particular technology in a system. It is always good to be able to talk through ideas with other data scientists, since no one has exactly the same perspective. The important thing to remember is that you are a team. No one expects one person to solve every problem on their own, so communicating problems as well as successes to your colleagues is key to success. That can be a tough thing for people who are more accustomed to academia to do!

What's your favorite part of your job? The most challenging part of your job?

My favorite part of the job is creating a plan of attack for a new problem: envisioning the steps necessary to take raw data and put it into a form that gives meaningful, actionable knowledge to the client. That's when I usually discover the most interesting new technologies, techniques, and models, and get to come up with the most creative ways to use those methods to process data given available resources. This is also the second most challenging part of the job, since there are often so many potential ways to solve a problem that determining which one is best can be very difficult, and sometimes even subjective. The most challenging part of the job, of course, is finding the source of a bug that is causing all of your model outputs to be nonsensical. You search your entire codebase for several hours, only to discover that you misplaced a comma somewhere two weeks ago that somehow never caused a problem until now!

Portrait of Dr. Andrew Graczyk

Dr. Andrew Graczyk

Dr. Andrew Graczyk is a graduate of The Data Incubator. He also earned his Ph.D. in economics from the University of North Carolina at Chapel Hill in December 2017. His research specialty in game theoretic modeling, Bayesian statistics, and time series analysis allowed him to synthesize novel models to capture adverse incentives responsible for behavior that other models struggle to explain. Prior to his career in data science, he developed experience working with a wide variety of data and topics from asset bubble formation to housing markets to environmental regulation and agriculture. As a senior data scientist at NNData, Dr. Graczyk applies his multifaceted experience with data and theory to create robust, flexible, and holistic solutions to problems using cutting-edge machine learning and statistical techniques.

Where Do Data Scientists Work?

The U.S. Bureau of Labor Statistics (BLS) tracks salary, industry, and location information for data scientists. As of May 2021, the BLS reports the following as the five top-employing states for data science professionals:

  • California
  • New York
  • Texas
  • North Carolina
  • Illinois

Job opportunities tend to cluster in urban areas. However, some data scientists work mainly or exclusively online. The position translates well to remote work.

The private sector dominates the data science employment landscape. As of May 2021, the BLS reports the following as the five leading industries for data scientist employment:

  • Computer systems design
  • Enterprise management
  • Technical consulting
  • Scientific research services
  • Credit mediation

A day in the life of a data scientist may differ depending on the industry of employment and whether a professional works on-site or remotely.

Common Questions About Data Scientists


What does a data scientist do day to day?

The day-to-day of a data science professional focuses on collecting and analyzing data to help solve organizational challenges. Data scientists also identify internal and external concerns that may benefit from advanced data analysis.

Do data scientists have a good work-life balance?

Data scientists usually work fixed, regular hours with some flexibility. This schedule can support a positive work-life balance. However, a typical day in the life of a data science professional varies depending on job demands. Longer hours are sometimes necessary.

Are data science jobs boring?

Some external observers view data science as a boring job. However, people with a knack for the field tend to enjoy their work and find it interesting. If data science sounds boring to you, it might not be the best career option.

Can data scientists work anywhere?

Many of the duties of a day in the life of a data scientist adapt well to remote work. As such, some employers support hybrid work schedules or 100% remote working arrangements.


Featured Image: gorodenkoff / iStock / Getty Images Plus

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