Data Scientist Skills

Use a simple, clean format that highlights your skills and experiences. Focus on readability by using bullet points and short phrases to present your skills. Properly organize your resume into education, experience, and skills sections.

“Adding a skills section to your resume is a great way to draw the recruiter’s attention to your most relevant strengths and competencies.”

– Ashley Watkins, NCRW, NCOPE, Job Search Coach

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.

As you list your responsibilities, it’s also important to highlight your specific achievements wherever you can.

“For soft skills, it’s often more effective to demonstrate them in the context of your past work experience. Instead of merely stating “excellent team player”, you are better off saying “collaborated with a cross-functional team of 6 on a new product launch that boosted sales by 30% in one year”. Examples of specific accomplishments or business outcomes speak louder than buzzwords.”

– Ana Lokotkova, Career Coach and Advisor

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.

If you’re applying for a role requiring a broad skillset, categorize your skills.

“If you opt to include a designated skills section on your resume, include up to 10 of your core competencies. Excessive skills lists are overwhelming and sometimes confusing to the reader.”

– Kelli Hrivnak, Marketing and Tech Recruiter

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.

Use numbers to quantify your achievements wherever possible. This helps potential employers understand your abilities and the impact you can have on their organization.

“Recruiters and hiring managers are looking for relevancy of how and when you applied those skills, so provide examples of this in your experience section. Bonus tip: Don’t just insert the skill like a task–include results.”

– Kelli Hrivnak, Marketing and Tech Recruiter

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.

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.

“The ‘one-size-fits-all’ approach doesn’t work when it comes to your resume. For every job application, tailor your skill set to match the job description. Most companies use Applicant Tracking Systems (ATS) that filter candidates based on keywords and skills listed in the job description. So, research the role you’re applying for and distill the skills required.”

– Ana Lokotkova, Career Coach and Advisor

In order to do this, “you must first understand what skills are most important for the target role,” says Ashley Watkins.

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%.

“Focus on the sought-after and in demand skills. A great way to figure out what is currently in demand is by researching current job openings from your preferred companies and reading through the posting. Pay close attention to the preferred requirements section and build your skills section based on this list.”

– Chelsea Jay, Career & Leadership Development Coach – Seasoned and Growing

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.

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.