What does a data scientist do?
A Data Scientist analyzes information from multiple sources in order to gain maximum insight that can give the company a competitive advantage. They work in different domains, including manufacturing, healthcare, education, and finance.
Data scientist responsibilities
Here are examples of responsibilities from real data scientist resumes:
- Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
- Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
- Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
- Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
- Perform cross-validation-test on linear regression model of data using scikit-learn.
- Develop python base statistical visualization to provide insights of fuzzy social media data.
- Perform data profiling and analysis, develop indicators/metrics, conduct trend analysis, and evaluate advance analytics tools.
- Key internal adviser on statistical modeling, machine learning, data validation, data visualization, and business intelligence processes.
- Implement clinical reporting programs that are utilized by both clinical and data management teams which aid in data visualization and reporting.
- Clean data using numpy and pandas.
- Work within company's MDM team.
- Assist students learning chemistry, biology, math
- Experience in maintaining the cluster on AWS EMR.
- Support stack for GPUs, used for TensorFlow models.
- Work on EMR to analyze data in S3 buckets.
Data scientist skills and personality traits
We calculated that 13% of Data Scientists are proficient in Python, Data Science, and Visualization. They’re also known for soft skills such as Logical thinking, Math skills, and Detail oriented.
We break down the percentage of Data Scientists that have these skills listed on their resume here:
- Python, 13%
Created categorization models in Python to identify customer loss and metrics to identify customers prior to discontinuation for improved retention.
- Data Science, 10%
Consult clients in Data science/statistical consultation Database development/design Machine learning (Classification, Regression etc.)
- Visualization, 5%
Implemented clinical reporting programs that were utilized by both clinical and data management teams which aided in data visualization and reporting.
- Java, 4%
Improved computational efficiency of 1-bucket-theta algorithm in Java by eliminating unnecessary input data in filter.
- Hadoop, 4%
Discovered interesting correlations between Wikipedia traffic volume spike and news events using R and Hadoop.
- Tableau, 3%
Designed and published visually rich and intuitively interactive Tableau workbooks and dashboards for executive decision making.
"python," "data science," and "visualization" are among the most common skills that data scientists use at work. You can find even more data scientist responsibilities below, including:
Logical thinking. One of the key soft skills for a data scientist to have is logical thinking. You can see how this relates to what data scientists do because "computer algorithms rely on logic." Additionally, a data scientist resume shows how data scientists use logical thinking: "prepared data visualization reports for the management using r. documented logical, physical, relational and dimensional data models. "
Math skills. Another soft skill that's essential for fulfilling data scientist duties is math skills. The role rewards competence in this skill because "computer and information research scientists must have knowledge of advanced math and other technical topics that are critical in computing." According to a data scientist resume, here's how data scientists can utilize math skills in their job responsibilities: "maintained statistics-based ab testing framework to test new text mining models. "
Detail oriented. data scientists are also known for detail oriented, which are critical to their duties. You can see how this skill relates to data scientist responsibilities, because "computer and information research scientists must pay close attention to their work, because a small programming error can cause an entire project to fail." A data scientist resume example shows how detail oriented is used in the workplace: "developed a predictive model on driver behavior based on the journey details and integrated with hadoop stream analytics. "
Analytical skills. data scientist responsibilities often require "analytical skills." The duties that rely on this skill are shown by the fact that "computer and information research scientists must be organized in their thinking and analyze the results of their research to formulate conclusions." This resume example shows what data scientists do with analytical skills on a typical day: "predicted sales growth of the next quarter using time series analysis to carry out an a/b testing. "
Communication skills. A commonly-found skill in data scientist job descriptions, "communication skills" is essential to what data scientists do. Data scientist responsibilities rely on this skill because "computer and information research scientists must communicate well with programmers and managers and be able to clearly explain their conclusions to people with no technical background." You can also see how data scientist duties rely on communication skills in this resume example: "developed data visualization materials and packaged communications on data analytics for presentation to technical and non-technical audiences. "
The three companies that hire the most data scientists are:
- Meta573 data scientists jobs
- Deloitte568 data scientists jobs
- General Dynamics518 data scientists jobs
Choose from 10+ customizable data scientist resume templates
Build a professional data scientist resume in minutes. Our AI resume writing assistant will guide you through every step of the process, and you can choose from 10+ resume templates to create your data scientist resume.
Compare different data scientists
Data scientist vs. Research and development internship
When it comes to Research and Development Internship, the duties will vary according to the organization or company. Most of the time, the responsibilities will revolve around observing the industry, taking part in the research and analysis, lend a helping hand in experiments and surveys, explore theories and attempt to create a model of out it, present findings for evaluation, and develop more innovative designs and systems. Moreover, in the Research and Development Internship, it always helps to be critical in solving complex problems.
These skill sets are where the common ground ends though. The responsibilities of a data scientist are more likely to require skills like "data science," "visualization," "hadoop," and "tableau." On the other hand, a job as a research and development internship requires skills like "c++," "c #," "powerpoint," and "html." As you can see, what employees do in each career varies considerably.
Research and development interns really shine in the health care industry with an average salary of $41,635. Comparatively, data scientists tend to make the most money in the start-up industry with an average salary of $123,913.research and development interns tend to reach lower levels of education than data scientists. In fact, research and development interns are 20.1% less likely to graduate with a Master's Degree and 12.3% less likely to have a Doctoral Degree.Data scientist vs. Developer analyst
A developer analyst is an individual who is responsible for building application requirements and develops database solutions that allow operational efficiency and user-friendly tools. Developer analysts are required to develop a detailed definition of business solutions that can include database design, data flow, and transaction processing requirements. They work with other teams to design and implement a web application that manages internal processes and can result in increased productivity. Developer analysts are also required to perform integration tests for various vendors for services that are according to business requirements and testing processes.
While some skills are similar in these professions, other skills aren't so similar. For example, resumes show us that data scientist responsibilities requires skills like "python," "data science," "hadoop," and "tableau." But a developer analyst might use other skills in their typical duties, such as, "c++," "eclipse," "html," and "architecture."
Developer analysts earn a lower average salary than data scientists. But developer analysts earn the highest pay in the finance industry, with an average salary of $99,277. Additionally, data scientists earn the highest salaries in the start-up with average pay of $123,913 annually.Average education levels between the two professions vary. Developer analysts tend to reach lower levels of education than data scientists. In fact, they're 16.8% less likely to graduate with a Master's Degree and 12.3% less likely to earn a Doctoral Degree.What technology do you think will become more important and prevalent for Data Scientists in the next 3-5 years?
Gabriel Foust
Computer Science instructor, Harding University
Data scientist vs. Operations research analyst
Operations research analysts are responsible for assisting organizations in making better decisions. These professionals work to develop solutions that will aid businesses to operate more efficiently using advanced techniques such as data mining, optimization, and mathematical modeling. They work closely with key organizational stakeholders to stay up-to-date on short-term and long-term business goals. They also conduct research that will give them insights they need to guide decision-makers and develop solutions using predictive modeling, simulations, and statistical analysis.
There are many key differences between these two careers, including some of the skills required to perform responsibilities within each role. For example, a data scientist is likely to be skilled in "data science," "hadoop," "tableau," and "data analytics," while a typical operations research analyst is skilled in "operations research," "dod," "c++," and "data analysis."
Operations research analysts earn the highest salary when working in the professional industry, where they receive an average salary of $115,452. Comparatively, data scientists have the highest earning potential in the start-up industry, with an average salary of $123,913.When it comes to education, operations research analysts tend to earn lower degree levels compared to data scientists. In fact, they're 12.5% less likely to earn a Master's Degree, and 9.5% less likely to graduate with a Doctoral Degree.Data scientist vs. Data analyst internship
A data analyst internship involves a trainee who wants to gain working experience in the field of information technology (IT) by assisting data analyst professionals. Data analyst interns should examine information using data analysis tools so that they can help their employers make important decisions by identifying various facts and trends. They write reports and present them to the management to provide new insights about new trends and areas for improvement. Data analyst interns can find work in areas such as banks, specialist software development companies, and consultancies.
Types of data scientist
Updated January 8, 2025
