Sample "Data Scientist Resume"

A Data Scientist Resume is a formal document used by a job applicant to introduce themselves to a future employer and showcase their academic and professional accomplishments in data science. If you want to build a successful career working with processing, analyzing, and drawing conclusions from various data to answer complicated questions and make significant decisions, your number one goal should be convincing the prospective employer of your sufficient skills and potential to improve the company that advertised the job vacancy. You can find a free Data Scientist Resume Sample through the link below.

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Data Scientist Resume Format

Although in most cases employers do not list requirements for resumes, if you want to draft the best Data Scientist Resume, you should follow these rules:

  1. Do not exceed one or two pages. Ideally, the entire resume should consist of one page - add a second only if you believe the description of your professional experience deserves more attention.
  2. Try to remain concise and polite. Short sentences and paragraphs will be easy to read and understand.
  3. Bold, italicize, and underline the most important points in the resume - headers, job titles, and your former employers. You must be consistent with the presentation so reread the resume before printing it out or submitting it online.
  4. Choose an appropriate font. It is recommended to use Times New Roman, Arial, Georgia, Garamond, or Tahoma. Font size should be between 10 and 12 points. If you want to keep all the details on one page, you may reduce the margins but leave some blank space anyway - the hiring manager may want to make remarks while evaluating your qualifications.
  5. Avoid present tense when talking about your former jobs and do not use first-person pronouns.
  6. To make sure the resume captures the attention of the reader, you should include strong action verbs and eliminate cliche words and phrases. The action verbs that can strengthen any resume are achieve, amplify, assemble, capitalize, execute, implement, pioneer, produce, stimulate, supervise.
  7. Trust the spell-check but scan the document before you send it with the job application. You may use any convenient file format - DOC or PDF are among the most common unless the employer specifically asks for something different.

How to Write an Effective Data Scientist Resume?

Here is a Sample Data Scientist Resume summary you may use for reference when composing your own document or customizing a Data Scientist Resume example you download here:

  1. Write down your name and contact information (a telephone number and an e-mail address) at the top of the page.
  2. Start with a resume objective. This short section of the document should outline your career aspirations and focus on your advanced skills. Do not forget to add the name of the position you are applying for - for example, title the document "Entry Level Data Scientist Resume".
  3. Describe your work experience. If you have worked for a few months or years, it is appropriate to list your jobs in chronological order. However, if you are a data scientist with more than ten years of experience or you believe your latest job should be noticed first by the potential employer, select a reverse chronological order. List all your former employers, your job titles, main duties, and dates of employment.
  4. Document the main details of your education even if it is not connected to the field you are working in - you should state the name of the college or university, the degree, and the number of years you spent studying.
  5. Prepare a list of your hard and soft skills. It may be a good idea to group and present them in two columns:
    • Hard skills indicate your technical abilities - things you have learned through formal education or in your previous jobs. A Junior Data Scientist Resume may highlight skills like knowledge of programming languages, coding, machine learning, debugging, statistics, data modeling and visualization, expertise in databases, and optimization;
    • Soft skills are more universal - they can be applied to any job, and if you are looking for other employment opportunities, you may copy-paste this entire section into a different resume. Key soft skills include critical thinking, communication with others, leadership experience, good time management, adaptability, and problem-solving.
  6. If you have any certifications, publications, or additional activities that may increase your employment chances, you should add them to the resume. For instance, record your volunteer work experience or the names of magazines that printed your articles about data science.

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Sample Data Scientist Resume
Contacts
Work Experience
linkedin.com/in/name
Data Scientist at Grubhub (August 2018 - current)
name@email.com
● Implemented various time series forecasting techniques to predict
surge in customer orders to lower average customer wait time;
(123) 456-78-90
● Deployed a recommendation engine to production to conditionally
New Brunswick, New Jersey
recommend other menu items based on past order history to increase
average order size by 5%;
● Designed a model in a Portland pilot program to increase incentives
Education
for drivers during peak ordering hours resulting in a 13% increase in
driver availability during peak ordering times.
B.S. Statistics, Rutgers
Data Scientist at Adobe (January 2016 - August 2018)
University, New Brunswick,
NJ, 2011-2015
● Worked with the product and marketing teams to identify which
customer interactions during their free trial maximize the likelihood of
conversions resulting in a conversion rate increase of 15%;
Skills
● Built a customer attrition random forest model that improved monthly
retention by 10 basis points for customers who were likely to attrit by
Python (NumPy, Pandas,
servicing relevant product features for them;
Scikit-learn, Keras, Flask)
● Worked closely with the product team to build a production
SQL (MySQL, Postgres)
recommendation engine in Python that improved the average length
on page for users and resulted in $275k in incremental annual revenue.
Git
Data Analyst at Adobe (April 2015 - January2016)
Time Series Forecasting
● Worked with product managers to perform cohort analysis that
Productionizing Models
identified an opportunity to reduce pricing by 20% for a segment of
users to boost yearly revenue by $610,000;
Recommendation Engines
● Built operational reporting in Tableau to find areas of improvement for
Customer Segmentation
contractors resulting in $190,000 in annual incremental revenue;
AWS
● Implemented a long-term pricing experiment that improved customer
lifetime value by 25%.
©
TEMPLATEROLLER.COM
Sample Data Scientist Resume
Contacts
Work Experience
linkedin.com/in/name
Data Scientist at Grubhub (August 2018 - current)
name@email.com
● Implemented various time series forecasting techniques to predict
surge in customer orders to lower average customer wait time;
(123) 456-78-90
● Deployed a recommendation engine to production to conditionally
New Brunswick, New Jersey
recommend other menu items based on past order history to increase
average order size by 5%;
● Designed a model in a Portland pilot program to increase incentives
Education
for drivers during peak ordering hours resulting in a 13% increase in
driver availability during peak ordering times.
B.S. Statistics, Rutgers
Data Scientist at Adobe (January 2016 - August 2018)
University, New Brunswick,
NJ, 2011-2015
● Worked with the product and marketing teams to identify which
customer interactions during their free trial maximize the likelihood of
conversions resulting in a conversion rate increase of 15%;
Skills
● Built a customer attrition random forest model that improved monthly
retention by 10 basis points for customers who were likely to attrit by
Python (NumPy, Pandas,
servicing relevant product features for them;
Scikit-learn, Keras, Flask)
● Worked closely with the product team to build a production
SQL (MySQL, Postgres)
recommendation engine in Python that improved the average length
on page for users and resulted in $275k in incremental annual revenue.
Git
Data Analyst at Adobe (April 2015 - January2016)
Time Series Forecasting
● Worked with product managers to perform cohort analysis that
Productionizing Models
identified an opportunity to reduce pricing by 20% for a segment of
users to boost yearly revenue by $610,000;
Recommendation Engines
● Built operational reporting in Tableau to find areas of improvement for
Customer Segmentation
contractors resulting in $190,000 in annual incremental revenue;
AWS
● Implemented a long-term pricing experiment that improved customer
lifetime value by 25%.
©
TEMPLATEROLLER.COM