Data Scientist Resume Example & Writing Guide

Use this Data Scientist resume example and guide to improve your career and write a powerful resume that will separate you from the competition.

Data scientists are some of the most sought after professionals in the job market right now. As the amount of data available to companies continues to grow exponentially, businesses need smart people who can make sense of it all.

Data scientists use their knowledge of statistics, computer science, and other disciplines to work with huge sets of information, analyze trends, identify opportunities, and develop solutions. They’re usually involved in every step of the research and development process, from brainstorming ideas to testing prototypes to rolling out finished products.

Data scientists are often thought of as the bridge between the tech world and the business world. They work with developers and engineers to build new products and services. They work with marketers and analysts to understand customers and create compelling content. They work with pretty much everyone to make sure the end result is something that people will love.

If you’re ready to join the ranks of this highly sought-after group of professionals and want to write a data scientist resume that will get you noticed, here are some tips and an example resume to help you do just that.

Michael Garcia
Los Angeles, CA | (123) 456-7891 | [email protected]

Data scientist with experience in a variety of industries, including retail, e-commerce, and FinTech. Proven ability to leverage data to improve business outcomes through analysis, modeling, and machine learning. Inquisitive mind and passion for problem solving drives results through data-driven decision making.

University of California, Berkeley Jun '10
M.S. in Statistics
University of California, Berkeley Jun '06
B.A. in Mathematics
Company A, Data Scientist Jan '17 – Current
  • Developed a deep learning model to predict the risk of mortality for patients based on their electronic health record data and clinical notes, with over 90% accuracy compared to manual review by physicians.
  • Designed an algorithm that leveraged natural language processing (NLP) techniques to extract structured information from unstructured medical records in real-time, resulting in a 10x reduction in the time required to process patient data during onboarding.
  • Built a recommender system using collaborative filtering algorithms to recommend personalized care plans for patients based on their disease state and treatment history, increasing physician efficiency by 20%.
  • Implemented a machine learning framework capable of detecting anomalies within large volumes of streaming IoT sensor data at millisecond resolution, reducing downtime across critical infrastructure such as oil pipelines by up to 50%.
  • Conducted research into methods for improving the performance of convolutional neural networks used in computer vision applications through adversarial training, achieving results comparable to those reported in academic literature while requiring only 1/10th the computational resources.
Company B, Data Scientist Jan '12 – Dec '16
  • Developed machine learning algorithm to predict customer churn based on behavioral data, reducing company’s cost of acquiring new customers by 15%
  • Built recommendation engine using collaborative filtering techniques that increased sales by 10% in one month
  • Created and maintained production-ready web application for collecting and analyzing large amounts of data
  • Collaborated with team members to create a comprehensive roadmap for the product’s development
  • Conducted A/B testing on all features before releasing them to production environment (increased efficiency)
Company C, Statistician Jan '09 – Dec '11
  • Conducted statistical analysis of data using methods such as regression, time series analysis, and multivariate analysis.
  • Presented results of statistical analysis in the form of tables, graphs, and written reports.
  • Developed and maintained statistical models to predict future trends.
  • SAS Certified Base Programmer
  • SAS Certified Advanced Programmer
  • SAS Certified Clinical Trials Programmer

Industry Knowledge: Machine Learning, Statistics, Data Visualization
Technical Skills: Python, R, Hadoop, Tableau, SAS, SQL
Soft Skills: Communication, Research, Data Analysis, Problem solving, Decision Making, Leadership, Creativity

How to Write a Data Scientist Resume

Here’s how to write a data scientist resume of your own.

Write Compelling Bullet Points

Bullet points are the most common way to showcase your experience and qualifications. But they don’t have to be boring or generic. You can use them to tell a story about your past experiences and demonstrate how you contributed to the organization.

For example, rather than saying you “analyzed data,” you could say you “analyzed data from customer surveys to identify trends in customer satisfaction and recommend changes to improve customer experience.”

The second bullet point is much more specific and provides more detail about what exactly you did and the results of your work.

Identify and Include Relevant Keywords

When you apply for a data scientist role, your resume is likely to enter an applicant tracking system (ATS). This program will scan your resume for specific keywords related to the job opening. If your resume doesn’t include enough relevant keywords, the ATS might automatically reject your application.

To increase your chances of getting an interview, use the most commonly used keywords on data scientist resumes as a starting point and then add in other relevant terms. Here are some of the most commonly used data scientist keywords:

  • Machine Learning
  • Python (Programming Language)
  • Data Science
  • R (Programming Language)
  • Data Analytics
  • Deep Learning
  • SQL
  • Statistics
  • Data Mining
  • Artificial Intelligence (AI)
  • Algorithms
  • Natural Language Processing (NLP)
  • Predictive Analytics
  • Apache Spark
  • Predictive Modeling
  • Hadoop
  • Data Visualization
  • Scala
  • Analytics
  • Databases
  • HBase
  • Java
  • Spark SQL
  • Apache Kafka
  • Scrum
  • Hive
  • MongoDB
  • Big Data
  • Machine Learning Library (MLlib)
  • Python Data Analysis Library (pydal)

Showcase Your Technical Skills

As a data scientist, you know that data doesn’t speak for itself. It needs to be analyzed, interpreted, and presented in a way that is easy for others to understand. And that’s where your technical skills come in. Recruiters are looking for data scientists who are proficient in programs like R, SAS, MATLAB, SPSS, and Stata, and who have experience with data mining, machine learning, and modeling. They also want to see that you have a solid understanding of big data concepts and platforms like Hadoop, Hive, and Spark.

So if you have experience with any of these programs or platforms, be sure to list them on your resume. And if you’re not familiar with them, now is the time to learn them!


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