Data Scientist Skills
Essential data scientist skills for a competitive edge. Based on our database of over 10 million real job listings.
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In the increasingly data-driven world, data scientists play a crucial role. They combine statistical analysis, data management, and advanced technology to turn large amounts of data into actionable insights.This guide provides an in-depth look at the critical data scientist and data science skills that can help you stand out in this competitive field.
How we got the data
The data in this report was pulled from Jobscan’s database of more than 10 million job descriptions and 17 million resumes.
We analyzed the job descriptions to find the skills that employers want the most. Then we analyzed the resumes to see which skills appeared most frequently.
Armed with this knowledge, job seekers can easily tailor their resumes and cover letters to highlight the most relevant skills for each job they apply to.
top 10 data scientist Hard Skills
top 10 data scientist Soft Skills
top 10 data scientist Skills on Resumes with High Match Rate
- Python
- Machine Learning
- Data Science
- Analytics
- Programming
- Engineering
- Statistics
- Algorithms
- Data Analysis
- Communication
Top 10 data scientist skills
Examples of how to write this skill on your resume:
- Leveraged Python libraries to preprocess and analyze large datasets, improving model training efficiency by 20%.
- Developed and deployed machine learning models using Python, leading to an improvement in prediction accuracy.
- Utilized Python for data visualization, aiding in presenting complex data insights to non-technical stakeholders.
Examples of how to write this skill on your resume:
- Implemented machine learning algorithms to predict customer churn, resulting in a reduction in customer loss.
- Developed a machine learning model to automate product recommendations, increasing sales conversion by 12%.
- Utilized machine learning techniques to analyze and interpret complex datasets, leading to more data-driven decisions.
Examples of how to write this skill on your resume:
- Performed detailed analytics to drive business strategy, contributing to a 15% increase in annual revenue.
- Utilized predictive analytics to identify potential market opportunities, increasing new customer acquisitions.
- Employed descriptive analytics to understand past performance, informing future strategy and decision-making.
Examples of how to write this skill on your resume:
- Led data science projects to optimize business processes, reducing costs by 30% and increasing efficiency.
- Applied data science principles to analyze and interpret complex datasets, leading to actionable business insights.
- Utilized data science methodologies to develop a predictive model for sales forecasting, increasing forecast accuracy.
Examples of how to write this skill on your resume:
- Applied statistical methods to analyze customer data, leading to a deeper understanding of customer behavior.
- Used statistical modeling to improve demand forecasting accuracy, leading to better inventory management.
- Utilized statistical techniques to validate data and ensure its reliability in strategic decision-making.
Examples of how to write this skill on your resume:
- Designed and implemented efficient data pipelines, ensuring timely data availability for analysis.
- Utilized engineering principles to improve the performance of data processing systems, reducing processing time by 25%.
- Built a robust data architecture to support the storage and analysis of large volumes of data.
Examples of how to write this skill on your resume:
- Applied computer science principles to optimize machine learning algorithms, increasing their performance and efficiency.
- Utilized understanding of data structures to manage large datasets effectively.
- Employed knowledge of computer science to build efficient data processing systems, enhancing their scalability and reliability.
Examples of how to write this skill on your resume:
- Proficient in several programming languages, enabling flexible and efficient data manipulation and analysis.
- Automated routine data cleaning and processing tasks using programming, increasing productivity by 20%.
- Developed scripts to automate data preprocessing and feature extraction, speeding up the model development process.
Examples of how to write this skill on your resume:
- Implemented advanced algorithms to solve complex data science problems, improving model performance by 15%.
- Optimized machine learning algorithms, reducing computational resources and speeding up training times.
- Applied understanding of various algorithms to choose the appropriate method for each data science task, improving overall project efficiency.
Examples of how to write this skill on your resume:
- Utilized strong mathematical knowledge to develop accurate statistical models for business forecasting.
- Applied mathematical principles to optimize machine learning algorithms, improving prediction accuracy.
- Leveraged understanding of linear algebra and calculus to handle high-dimensional data effectively.
Optimize your resume in minutes
5 tips for writing a data scientist resume
Create a bulleted resume skills section
Use a simple, clean format highlighting your skills and experiences. Focus on readability using bullet points and short phrases to present your skills. Properly organize your resume into education, experience, and skills sections.
For example, a data scientist’s bullet points might look like this:
- Understanding of various algorithms
- Programming and machine learning
- Data processing systems
Hitting all the top job requirements with your skills list will help you rank highly for a keyword search within an applicant tracking system. But don’t stop there. Add context for every skill elsewhere in your work experience. If a recruiter is excited by data science in your skills section, they’ll first skim your work experience to figure out when, how, and how much you used that skill.
Highlight skills and achievements in your work experience section
As a data scientist, you must emphasize your skills and achievements in the work experience section of your resume. This will make you stand out to potential employers and show them you possess the expertise they seek.
When detailing your work experience, showcase your accomplishments relevant to the data scientist’s role. For instance, if you’ve excelled in programming, make a point to highlight these achievements.
Instead of saying,
“Developed scripts to automate data.”
You could say,
“Developed scripts to automate data preprocessing and feature extraction, speeding up the model development process.”
Using these detailed and quantified statements, you can effectively highlight your programming skills and achievements, making your resume more attractive to potential employers.
Break resume skills sections into categories
If you’re applying for a data scientist role that requires a diverse skillset, consider categorizing your skills to make it easier for potential employers to understand your range of expertise.
For instance, someone applying for a data scientist role might benefit from segmenting their skill lists as follows:
Programming and Technical Skills:
- Python: Leveraged Python programming to manipulate large datasets, automate data processing tasks, and implement machine learning algorithms.
- Programming: Used various programming languages, such as R and Java, to develop data preprocessing and model development scripts.
Machine Learning and Analytics Skills:
- Machine Learning: Applied machine learning algorithms to develop predictive models, enhancing business decision-making.
- Analytics: Utilized analytics tools to provide actionable insights from complex datasets, supporting strategic decision-making.
Statistics and Data Science Skills:
- Statistics: Applied statistical methods to analyze datasets and extract meaningful insights.
- Data Science: Leveraged data science methodologies to drive data-driven decisions and create business value.
Engineering and Computer Science Skills:
- Engineering: Utilized engineering principles to design and implement efficient data processing systems.
- Computer Science: Applied computer science principles to solve complex data-related problems, enhancing data processing and analysis.
Algorithm and Mathematics Skills:
- Algorithms: Implemented advanced algorithms to solve complex data science problems, improving model performance.
- Mathematics: Utilized strong mathematical knowledge to develop accurate statistical models for business forecasting.
Please note that while the skills are largely the same, the way they’re categorized and presented can make a significant difference in how they’re perceived by potential employers.
This method of categorizing skills allows potential employers to understand your diverse expertise quickly and makes your resume more readable.
Quantify your achievements
Use numbers to quantify your achievements wherever possible. This helps potential employers understand your abilities and the impact you can have on their organization.
Instead of saying,
“Implemented advanced algorithms to solve complex data.”
You could say,
“Implemented advanced algorithms to solve complex data science problems, improving model performance by 15%.”
Using numbers gives hiring managers a better sense of your value and demonstrates your expertise in the data scientist role.
Quantifying your achievements makes your resume more compelling and effectively communicates the impact you can have on a potential employer’s organization.
Tailor your resume to the job description
Read the description carefully and emphasize the relevant skills and experiences. Highlight the skills that the employer seeks and provide examples of how you’ve used those skills in your previous roles. Doing so can demonstrate that you’re a strong fit for the position and increase your chances of being invited to an interview.
Job Title: Data Scientist at AI Company
Requirements:
Experience with Python programming, machine learning, data analytics, data science methodologies, statistical analysis, and algorithm development.
Tailored Resume Description:
- Utilized Python and machine learning techniques to develop and deploy predictive models that increased operational efficiency by 30%.
- Leveraged data analytics and data science methodologies to provide actionable insights from complex datasets, driving strategic decision-making and contributing to a 20% increase in annual revenue.
- Applied statistical methods and advanced algorithms to analyze large datasets, leading to a better understanding of market trends and a 15% increase in marketing campaign effectiveness.
- Employed strong mathematical knowledge and engineering principles to design efficient data processing systems, improving data handling capacity by 40%.
By tailoring your resume in this way, you can demonstrate that you have the specific skills and experiences the employer is looking for, thus increasing your chances of getting an interview.
Bonus Tip: Use action verbs
Start each bullet point with an action verb. An action verb expresses an action, such as “create,” “build,” “manage,” “lead,” or “implement.”
Action verbs grab the reader’s attention and paint a vivid picture of what you accomplished at work.
Action verbs make your resume more interesting to read. They also show the kind of can-do attitude that employers are looking for.
FAQs
You need technical, mathematical, and soft skills as a data scientist. On the technical front, you should be proficient in programming languages like Python and R. Familiarity with SQL and databases is also crucial. You should have a strong understanding of machine learning, data analytics, statistics, and algorithm development. On the mathematical side, skills in statistics, calculus, and linear algebra are important. As for soft skills, you need strong problem-solving abilities, excellent communication skills, and a knack for visualizing and presenting data that non-technical stakeholders can understand.
The most important data scientist job skills to have on your resume include proficiency in Python or R, knowledge of machine learning techniques, and experience with data analytics. Demonstrated ability to apply statistics and mathematics to solve complex business problems, experience in data visualization, and strong communication skills are also highly valued.
While all skills can add value, some may be less critical for a data scientist role. For instance, knowledge of less commonly used programming languages may be less important. Also, while soft skills like teamwork and leadership are beneficial, they may carry less weight than more technical, role-specific skills.
Two of the most important hard skills for data scientists are proficiency in programming (Python is most commonly sought after) and a deep understanding of machine learning techniques. These skills allow data scientists to manipulate data, build predictive models, and derive actionable insights to inform strategic decision-making.
