Day-to-day responsibilities

Here are some of the core responsibilities data scientists manage in their day-to-day work:

• Collecting and cleaning data
  Data scientists must gather and integrate information from various sources and ensure it’s accurate, consistent, and usable. This often means dealing with incomplete, unstructured, or inconsistent data and applying preprocessing steps to remove errors and ensure that formatting is standardized across entire data sets.
• Analyzing data
  Data scientists examine datasets to identify trends, outliers, and patterns. This often includes exploratory data analysis (EDA), statistical testing, and data cleaning to prepare information for more-in-depth evaluation.
• Creating algorithms
  These professionals design and implement algorithms to efficiently process data and extract relevant information. These can range from basic classification rules to more advanced machine learning (ML) models, depending on the complexity of the problem being addressed.
• Developing predictive models
  Using historical data, data scientists create models that forecast outcomes or simulate scenarios. These models help organizations anticipate customer actions, detect fraud, make accurate market predictions, or otherwise optimize business operations.
• Communicating insights to stakeholders
  Analysis is only one side of the coin; effective data scientists also need to be able to explain their findings through visualizations, dashboards, and reports. Powerful communication skills make it possible to translate technical results into clear, actionable recommendations for both technical and non-technical audiences. 

Data is as ubiquitous in modern business as electricity-constantly flowing and essential to operations in every sector. And as data continues to shape nearly every aspect of commerce, demand for professionals who can extract insight from that data is widespread. Organizations across the globe use data scientists to get more out of their data. Although this is certainly not a comprehensive list, here are several industries that actively employ data scientists:

• Information technology
  IT companies often lead the way in hiring data scientists, using them to improve software performance, power AI features, personalize user experiences, and uncover usage patterns to inform product development and guide roadmap planning.
• Finance
  Financial institutions count on data scientists to support fraud detection systems and build models for credit scoring. They also help investment firms improve forecasting strategies by analyzing market data and evaluating risk in real time.
• Healthcare
  In healthcare, data scientists work with patient records, treatment outcomes, and operational metrics to improve care delivery. Their work also supports the use of predictive analytics to support early detection of medical conditions.
• Research
  Academic and scientific research relies heavily on data scientists to manage and interpret large datasets. In everything from running clinical trials to analyzing social behavior, researchers depend on data experts to uncover evidence that supports or disproves hypotheses.
• Life sciences
  Pharmaceutical and biotech companies work with data scientists to analyze data and patient responses during drug trials. Their findings help accelerate drug development and support the creation of targeted therapies based on individual patient profiles.
• Insurance
  In the insurance industry, data scientists are instrumental in improving how policies are priced and how risk is evaluated. Their work likewise helps detect suspicious claims activity.
• Retail
  Data scientists play a key role in retail, where they work to better understand customers across both online and in-store channels. By analyzing sales trends and tracking inventory movement, businesses can make smarter decisions about promotions, pricing, stock levels, and more. 
• Telecommunications
  Telecom companies use data scientists to monitor call and data usage patterns, helping them identify network issues quickly and reduce customer churn. Data also plays a role in guiding service upgrades and optimizing infrastructure investments.
• Government
  In the public sector, data scientists contribute to everything from budget planning to fraud detection. They assist with large-scale data projects related to public health, national security, and transportation, helping agencies allocate resources more effectively.

Analysis is a major part of what a data scientist does, so it is no wonder that the roles of data scientist and data analyst tend to get grouped together. But while these terms are sometimes used interchangeably, data scientists and data analysts perform distinct functions:

Data scientist

Data scientists are typically tasked with solving open-ended problems using a combination of programming, statistical modeling, and machine learning. They design experiments, create predictive models, and develop custom algorithms to extract insights from complex, (often unstructured) data. Their work generally begins with identifying what questions need to be asked to address specific needs, and then determining how data can be used to answer those questions.

Data analyst

Data analysts focus more on examining existing datasets to uncover trends, generate reports, and answer predefined business questions. They work primarily with structured data and use querying, aggregation, and data visualization to support operational and strategic decisions. While their analyses are critical for operational insights, they typically do not build models or deploy more advanced machine learning solutions.

In simple terms, data analysts explain what has happened, while data scientists use data to predict what could happen next-and why.

Data scientists combine multiple disciplines to turn raw data into strategic insight. That means that to be effective, they need technical proficiency alongside proficiency in communication, problem-solving, and business alignment. This blend of hard and soft skills is what allows data scientists to analyze information and ensure its usability within a business context.

Technical skills

Technical (or hard) skills refer to the hands-on abilities required to collect, process, analyze, and model data. These are foundational to the data scientist’s workflow and are often what hiring managers prioritize when assessing candidates. Hard skills include:

• Programming
  Most data scientists are proficient in various programming languages (such as Python, R, or SQL). This allows the data scientist to work within the backend of applications to build models and automate tasks across various stages of the data pipeline.
• Statistical analysis
  Understanding statistical concepts allows data scientists to test assumptions and validate outcomes. These skills help ensure that models are based on sound data patterns rather than coincidence or bias.
• Machine learning
  Data scientists often build predictive tools that learn from historical data. Knowing how to apply supervised and unsupervised learning methods enables them to develop solutions that adapt over time and deliver more accurate results.
• Data visualization
  The ability to present data visually is essential for making insights accessible. Clear charts and dashboards help non-technical stakeholders understand what the data is showing and why it matters.
• Data management
  Before any analysis can begin, data must be cleaned, formatted, and organized. Data scientists spend significant time ensuring that datasets are complete and ready for use.
• Big data tools
  In environments with high-volume or fast-moving data, traditional tools may fall short. Platforms like Hadoop and Apache Spark support large-scale processing and are often used when working with massive datasets.

Soft skills

Soft skills are what give data scientists the power to work effectively with people and within business contexts. These skills help ensure that their technical work has real impact across the organization.

• Business acumen
  Understanding company goals and operational challenges helps data scientists focus their work on high-value opportunities. This context is necessary to define meaningful questions and measure the impact of the insights they produce.
• Communication
  Explaining findings in plain language helps build trust and buy-in from non-technical colleagues. Data scientists must present complex results in a way that supports decision-making.
• Critical thinking
  Good data scientists evaluate their own assumptions and ask thoughtful questions about the data. This mindset helps them avoid misleading conclusions and discover deeper insights.
• Curiosity
  A strong desire to explore data often leads to discoveries that others might overlook. Curiosity drives experimentation and the pursuit of additional or unexpected findings.
• Collaboration
  Most projects require input from cross-functional teams. Data scientists must work well with engineers, business leads, and other stakeholders to ensure their work supports broader goals.
• Adaptability
  Technologies change quickly, and priorities can shift based on business needs. Data scientists must be comfortable learning new tools and adjusting their approach as needed.

The path that leads to a career in data science is not the same for everyone-some enter the field through traditional academic programs, while others build their skills through alternative methods or experience gained while working in adjacent fields. Regardless of the route, what matters most is the ability to work with data effectively and show employers that you can apply those skills in real-world scenarios.

Educational requirements

Many data scientists hold a formal degree in a technical or quantitative field. A bachelor’s degree in computer science, statistics, mathematics, or data science provides a strong foundation. These programs typically cover core concepts such as data structures, probability, linear algebra, and statistical analysis-along with essential programming experience.

A master’s degree in data science, analytics, or a closely related field allows professionals to deepen their knowledge of machine learning, big data systems, and advanced modeling. While not required for every role, a graduate degree can be useful for candidates looking to specialize or pursue more senior-level positions.

Alternative pathways to data science

A traditional degree isn’t the only way to enter the field. Bootcamps, online courses, and professional certificate programs can offer targeted instruction and practical training. These programs are often structured to help individuals build job-ready skills, and are typically more focused and accelerated than traditional education.

Many of these alternative paths also include hands-on projects that simulate real business challenges. This can be especially valuable for career changers or those who want to quickly gain relevant experience. Some learners use these programs to supplement existing degrees.

Building a portfolio

In data science, employers want more than a resume or a degree-they want to see how you think and what you can build. A strong portfolio gives you a chance to show your problem-solving approach and technical abilities through completed projects.

Portfolios can include personal projects, case studies, or challenges that you have addressed using data science skills. Hosting work on platforms like GitHub allows others to view your code and see the outcomes of your analysis. Good portfolios often provide clear documentation, visualizations, and context around the problem being solved.

Preparing for data science interviews

Landing a data science role usually involves a multi-stage interview process, including both technical and behavioral assessments. Candidates may be asked to solve programming problems, explain statistical concepts, or interpret data from a provided data set. Many employers will also evaluate how well you communicate your thinking-especially when discussing trade-offs or limitations in your approach.

Practicing common coding challenges and reviewing foundational concepts in statistics and machine learning is a good way to prepare. Some companies may also include take-home assignments or live problem-solving sessions. Preparing examples from past projects-and being ready to explain your role and reasoning-can help you stand out.

The work of a data scientist is directly connected to the tools they use-from collecting and managing raw data to developing predictive models and communicating results, every step in the process depends on reliable technologies. Below are some of the most essential technologies data scientists use on the job:

Programming languages for data science

Programming is foundational to the data science workflow, and different programming languages are suited to different tasks.

• Python
  Widely used in the field for its readability and broad ecosystem, Python supports data analysis, machine learning, and automation through libraries like pandas, NumPy, scikit-learn, and TensorFlow. 
• R
  Preferred in many academic and research settings, R excels at statistical analysis and data visualization.
• SQL
  SQL is essential for retrieving and managing structured data from relational databases. It’s a core skill for accessing production data and preparing it for analysis.
• SAS
  Most common in legacy systems, SAS is still widely used across healthcare and finance sectors. SAS is valued for its reliability and support for large-scale statistical modeling.
• Scala and Java
  These languages are often most effective in big data environments, especially when working with platforms like Apache Spark for distributed data processing. 

Data visualization tools

Data scientists rely on visualizations to make their findings understandable and actionable. Well-designed visuals can reveal patterns, clarify trends, and provide context that raw numbers cannot.

Common tools used for creating data visualizations include:

• Generative AI (GenAI)
• Tableau
• Power BI
• Plotly 
• Matplotlib
• Seaborn
• ggplot2

ML and AI frameworks

Thanks to recent advances, ML and artificial intelligence (AI) are taking on a more central role in data science. The frameworks and libraries that support these capabilities help data scientists build, train, and deploy models with greater efficiency and accuracy.

To excel as a data scientist, Learn the method of analyzing data with advanced AI courses and become familiar with the following frameworks:

• Scikit-learn
• TensorFlow
• PyTorch
• XGBoost 
• LightGBM
• Keras

Despite the powerful tools available, data science work is rarely straightforward. Many of the obstacles data scientists face aren’t technical-they're logistical, strategic, or communication-related. Solving these challenges requires a combination of technical skill and business awareness. Below are two of the most common hurdles data scientists encounter.

Data quality and cleaning

Clean data is essential for trustworthy results, but in practice, data is often inconsistent or incomplete. Even small errors-like missing values or duplicate entries-can distort analysis and reduce the reliability of a model.

To address these issues, data scientists use preprocessing methods to identify and correct problems before modeling begins. Tools like pandas in Python or tidyr in R help automate these steps. Standardizing data validation rules at the point of entry can also prevent problems from compounding over time. In many organizations, this work involves coordination with data engineers or system owners to align on how information is collected and maintained.

Communicating complex findings

Even the best analysis can fail to make an impact if the findings are not clearly communicated. Data scientists often need to explain technical models and statistical outcomes to non-technical stakeholders-people who may not understand, or need to understand, the underlying math.

To make insights useful to the intended audience, the message must be clear and concise. Visual aids and plain-language summaries help decision-makers understand the significance of the findings. When data scientists focus on implications rather than methodology, their work becomes easier to act on and more likely to influence strategy.

Data scientists continue to be among the most sought-after professionals in today’s business landscape. And as organizations increasingly adopt AI-powered solutions,  the need for skilled individuals who can interpret data, build models, and translate insights into action continues to grow. To meet that need, aspiring data scientists must keep pace with new technologies and sharpen the skills that set them apart.

ServiceNow University provides learners with a flexible, accessible way to build expertise in analytics, automation, and AI, centered around an in-depth understanding of how to take full advantage of the ServiceNow AI Platform®. ServiceNow University further supports current and upcoming data scientists by offering free, on-demand training as well as instructor-led courses, designed to help you earn essential certifications and build the skills you need to succeed. The following courses may be particularly useful for prospective data scientists:

• Platform Analytics Fundamentals
  This course introduces core analytics capabilities in ServiceNow, including how to monitor KPIs, configure formula indicators, and build dashboards. It also covers essential.
• Platform Analytics Advanced
  A step beyond the fundamentals, this course focuses on advanced data modeling and dashboard design. Topics include scripted breakdowns, interactive dashboards, and external metrics. It also prepares learners for the Certified Implementation Specialist - Platform Analytics exam.
• Performance Analytics Advanced (On-Demand)
  A flexible, digital version of a Performance Analytics Advanced course, this option makes it possible for learners to build deeper trend analysis and visualization skills at their own pace.
• Understand Data Pills and Data Streams in Flows
  This microlearning course explains how data pills are used to pass information between flow actions and how data streams manage record collections. Learners practice identifying, configuring, and troubleshooting data movement within Flows using these tools.
• Workflow Data Fabric (Learning Path)
  The Workflow Data Fabric learning path explores how to connect, understand, and act on data across workflows using ServiceNow’s unified platform. It focuses on combining AI, data, and workflows to support real-time decision-making and drive strategic impact.

ServiceNow empowers data scientists to accelerate their impact. See how you can maximize your skills and drive better results-for your company, and for yourself. Click here to explore available courses.