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Data scientist skills for your resume and career

Updated January 8, 2025

4 min read

Quoted experts

Yingfu (Frank) Li Ph.D.,

Michael Gallaugher Ph.D.

Some of the most important hard skills a data scientist can demonstrate on their resume are data analysis and data visualization because these skills make up a large bulk of the job. It's also important for data scientists to have experience working with software such as Hadoop, and programming languages such as Python.

When it comes to soft skills, data scientists should have strong critical thinking skills above all else. Data scientists need to find solutions and be open to changes in plans, so adaptability skills are also crucial.

Below we've compiled a list of the most critical data scientist skills. We ranked the top skills for data scientists based on the percentage of resumes they appeared on. For example, 13.5% of data scientist resumes contained python as a skill. Continue reading to find out what skills a data scientist needs to be successful in the workplace.

15 data scientist skills for your resume and career

1. Python

Python is a widely-known programming language. It is an object-oriented and all-purpose, coding language that can be used for software development as well as web development.

Here's how data scientists use python:

Created categorization models in Python to identify customer loss and metrics to identify customers prior to discontinuation for improved retention.
Worked on Natural Language Processing with NLTK module of python for application development for automated customer response.

2. Data Science

Data science refers to a multidisciplinary discipline that utilizes scientific techniques, procedures, frameworks, and structures to derive information and observations from various organized and irregular data sets.

Here's how data scientists use data science:

Consult clients in Data science/statistical consultation Database development/design Machine learning (Classification, Regression etc.)
Completed Microsoft Professional Program in Data Science.

3. Visualization

Here's how data scientists use visualization:

Implemented clinical reporting programs that were utilized by both clinical and data management teams which aided in data visualization and reporting.
Designed and developed data wrangling and visualization techniques as well as a classification engine based on Logistic Regression.

4. Java

Java is a widely-known programming language that was invented in 1995 and is owned by Oracle. It is a server-side language that was created to let app developers "write once, run anywhere". It is easy and simple to learn and use and is powerful, fast, and secure. This object-oriented programming language lets the code be reused that automatically lowers the development cost. Java is specially used for android apps, web and application servers, games, database connections, etc. This programming language is closely related to C++ making it easier for the users to switch between the two.

Here's how data scientists use java:

Improved computational efficiency of 1-bucket-theta algorithm in Java by eliminating unnecessary input data in filter.
Implemented using Java, C#, neighbor algorithm, metric smoothing algorithm, IDEAL machine learning library, and Oracle.

5. Hadoop

Hadoop is an open-source software and procedures framework that is free for anyone to use on the internet. Hadoop aids in big data operations. It allows massive data storage, applications to be run on commodity hardware, and can easily manage to run various tasks occurring at the same time.

Here's how data scientists use hadoop:

Discovered interesting correlations between Wikipedia traffic volume spike and news events using R and Hadoop.
Played integral part in transitioning company from relational databases to Hadoop based data-store.

6. Tableau

Here's how data scientists use tableau:

Designed and published visually rich and intuitively interactive Tableau workbooks and dashboards for executive decision making.
Consulted with and trained analysts to effectively access data and optimize Tableau dashboards.

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7. Data Analytics

Here's how data scientists use data analytics:

Advance a LLNL program's fundamental understanding of advanced data analytics by modernization of related processes using applied research and development.
Utilized advanced methods of big data analytics, machine learning, artificial intelligence, wave equation modeling, and statistical analysis.

8. Data Visualization

Data visualization is the process of presenting data in a more beautiful, elegant, and descriptive way in front of others using visual elements such as charts, graphs, maps, or any other type of visual presentation. This makes the data more natural for the human mind to comprehend and thus makes it easier to spot trends, patterns, and outliers within large data sets.

Here's how data scientists use data visualization:

Provided clients with high quality data visualizations including static plots, animations, and interactive Shiny applications.
Developed technical documents and reports, and designing data visualizations to communicate complex analysis results.

9. TensorFlow

Here's how data scientists use tensorflow:

Claim Damage Estimate: use deep learning tool TensorFlow for image classification on damaged cars.
Lead TensorFlow based deep learning workshops for CME employees.

10. Machine Learning Techniques

Here's how data scientists use machine learning techniques:

Provided statistical support including statistical modeling and machine learning techniques for internal research and provided recommendations based on the results.
Identified areas of improvement in existing business by unearthing insights by analyzing vast amount of data using machine learning techniques.

11. Predictive Models

In an effort to understand what could happen in the future, predictive models provide the statistical analytics to estimate the possibilities. With predictive modelling, relevant data is collected for the subject that requires forecasting, analysed and modelled to create different outcomes. The importance of this is to provide a basis for decision making that will foster a desired outcome for a business or government.

Here's how data scientists use predictive models:

Developed explanatory/ predictive models using independent variables (manufacturing process variables, raw material attributes) to predict critical parameters.
Researched methods to improve statistical inferences of variables across models and developed statistical, mathematical and predictive models.

12. Machine Learning Algorithms

Machine learning algorithms involve the engines of machine learning. It consists of the algorithms that turn a data set into a model.

Here's how data scientists use machine learning algorithms:

Executed massive voice-of-customer initiative by scrapping twitter feeds and developing machine learning algorithms to assess the sentiment of each tweet.
Implemented supervised machine learning algorithms to predict the engine performance based on the selected features using multivariate regressions.

13. Neural Networks

Here's how data scientists use neural networks:

Developed a predictive model and validate Neural Network Classification model for predict the feature label.
Included decision trees, support vector machines, genetic programming, neural networks, distance correlation and mixture models.

14. Scala

Scala is a modern programming language with multiple paradigms with which common programming models and patterns can be concisely, elegantly, and reliably expressed. Scala was created by Martin Odersky and published the first version in 2003. It combines functional and object-oriented programming in a concise high-level language. Many of Scala's design decisions are aimed at addressing criticism of Java. It interoperates seamlessly with both Java and Javascript. It is strongly seen as a static type language and does not have a primitive data concept.

Here's how data scientists use scala:

Developed Spark code using Scala and Spark-SQL/Streaming for faster testing and processing of data.
Learned Apache Spark and Scala on the job with real world projects.

15. Machine Learning Models

Here's how data scientists use machine learning models:

Developed and implemented new generation machine learning models for customer behavior prediction, optimal line assignment, and transaction fraud detection.
Developed machine learning models utilizing Logistic Regression and Random Forests to identify human characteristics and behavior to derive striking insights.

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What skills help Data Scientists find jobs?

Tell us what job you are looking for, we’ll show you what skills employers want.

What skills stand out on data scientist resumes?

Yingfu (Frank) Li Ph.D.

Program Chair of Statistics and Associate Professor of Statistics, University of Houston - Clear Lake

Statistical computing and communication skills

What soft skills should all data scientists possess?

Michael Gallaugher Ph.D.

Assistant Professor, Elected Director of The Classification Society, Baylor University

From the beginning, statistics have been very interdisciplinary and have become even more so in recent years. With that comes working with people with various backgrounds, including those who have only a very basic understanding of mathematics and statistics. Therefore, a statistician needs to reduce the mathematical and computational jargon to simple language.

What hard/technical skills are most important for data scientists?

Michael Gallaugher Ph.D.

Assistant Professor, Elected Director of The Classification Society, Baylor University

With the types of data being analyzed today, computational and coding skills are key. Anyone entering the statistics field, regardless of going into academia or industry, should be comfortable coding in at least one statistical computing language such as R, python, or more recently, Julia. In addition, and this is probably obvious, strong mathematical skills are also very important.

What data scientist skills would you recommend for someone trying to advance their career?

Neil Rothman Ph.D.

Professor and Program Coordinator, Stevenson University

Any gap year experience should be complementary to their degree program and career goals. If they lack a specific skill that is important in their field of choice, they should focus on that. Otherwise, any experience is useful knowledge, but an experience that provides a better perspective on why they are pursuing a particular career would be best. Most jobs require a multidisciplinary approach to problem-solving, but most degree programs don't necessarily provide this. Software development and data analysis will be crucial in almost any career, so that might be something to look at (e.g., Python, R, etc.).

What type of skills will young data scientists need?

Gagan Agrawal

Professor, Augusta University

I feel that the skill set needed in computing fields has held quite steady for some amount of time now. You will need a combination of basic software and problem-solving links. You need the ability to work on projects with others and learn new languages or technologies on your own. Communication skills are always important. Companies like graduates who have taken the initiative and done projects outside classwork - this shows that you really enjoy work, and you are motivated and driven.

Lately, there are specialized sectors like security or Artificial Intelligence/Machine Learning that are seeing a lot of action. If you want to be in either of those spaces, you need to take electives accordingly and probably do projects in these areas on your own (and/or possibly take online classes). But then, there are classical areas, like database programming, that still employ many.

What technical skills for a data scientist stand out to employers?

Lenny Fukshansky Ph.D.

Professor of Mathematics, Claremont McKenna College

I believe that the industry employers are constantly looking for people with a combination of strong analytic background (including mathematical modeling, statistics, and programming knowledge), good communication skills and leadership potential. We are obviously observing a rapid surge of data science, but it is important to keep in mind that just the familiarity with current data handling techniques is not sufficient for a successful career going forward. This knowledge has to rest on the understanding of fundamental mathematical and computer science apparatus from which data science has emerged. As such, I would recommend a major in mathematics, complemented by some data science courses or a minor.

Do you want to know more?

List of data scientist skills to add to your resume

The most important skills for a data scientist resume and required skills for a data scientist to have include:

Python
Data Science
Visualization
Java
Hadoop
Tableau
Data Analytics
Data Visualization
TensorFlow
Machine Learning Techniques
Predictive Models
Machine Learning Algorithms
Neural Networks
Scala
Machine Learning Models
SAS
Statistical Analysis

Natural Language Processing
AWS
Pandas
Scikit-learn
Data Collection
Keras
Math
Predictive Analytics
Regression
Power Bi
B Testing
MATLAB
Text Mining
Statistical Methods
Statistical Models
Azure
Time Series Analysis

NumPy
Decision Trees
Extraction
ETL
Linux
NoSQL
GIT
A/B
Exploratory Data Analysis
BI
Logistic Regression
Strong Analytical
Patients
Cloud Computing
SPSS

Updated January 8, 2025

Zippia Team

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

Updated January 8, 2025

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