Example of a Data Scientist Resume

In order to be considered for indemand data scientist positions, it makes a difference when you have a current and updated resume. More often than not, recruiters will only read a resume if it passes through the applicant tracking system software that they are using. The ATS scans resumes for relevant keywords and phrases for their specific industry. It discards resumes that do not meet certain requirements for specific positions.

You can save time by downloading our data scientist resume sample and fill in your personal information as you see fit. Then you can revise your resume for each job that you want to apply to by adjusting the content according to each organization’s requirements. This means that you must carefully read each job posting and add the relevant keywords while removing any repetitive or unnecessary information.

Avoid using phrases and abbreviations. You can add or remove sections but keep in mind that a well written resume should be no more than two pages long and have clear and consistent formatting. Tables and columns are not recommended. Each section should have a standard heading. If you need to build an entry-level resume from scratch and you do not have a lot of information, you can utilize other sections to add necessary keywords.

Some ideas to try are;

Including an objective or professional summary,
Emphasizing your educational experience by listing relevant courses taken, any achievements and certifications obtained,
Adding your volunteer work,
Creating a list of applicable skills and technical expertise.

Anna Smith
(555) 555-5555, name@email.com

LinkedIn.com/anna.smith

Provides analysis-driven, action-oriented solutions to challenging business problems. I’m a business-minded data scientist with a demonstrated ability to deliver valuable insights via current analytics and advanced data-driven methodologies. A reliable go to as a key advisor in driving global, multibillion-dollar growth with gains in customer loyalty and record-setting profit improvements.

SKILLS / TECHNICAL EXPERTISE:

Data & Quantitative Analytics

Decision Making Analytics

Predictive Modeling

Data-Driven Personalization

KPI Dashboards & BPI Plans

Big Data Queries & Interpretation

Data Mining & Visualization Tools

Machine Learning Algorithms

Business Intelligence

Research, Reports & Forecasts

WORK EXPERIENCE:

20XX to Present

Senior Analyst, Company One (Subscriber-based provider of streaming digital movies & TV)

Contributes towards two year revenue growth from $1.2B to $3.25B.
Achieves an 87% renewal rate (15% above goal) in 2017.
Boosts market share by 16%, customer satisfaction by 25% and mobile users by 350% in 20XX.
Furnishes executive leadership team with insights, analytics, reports and recommendations that enable effective strategic planning across all business units including distribution channels and product lines.
Develops intricate algorithms based on a deep-dive of statistical analysis and predictive data modeling that is used to deepen relationships, strengthen longevity and personalize interactions with customers.
Analyzes and processes complex data sets using advanced querying, visualization and analytics tools.
Identifies, measures and recommends improvement strategies for KPIs across all business areas.

Teaching Appointments: Teaches undergraduate and graduate-level courses in statistics and economics as an adjunct faculty member at ABC University (20XX to Present) and XYZ University (20XX to 20XX).

EDUCATION:

Ph.D. in Statistics

MA in Behavioral Economics

University One

BS in Mathematics, Minor in Computer Science
University Two

SKILLS:

Data & Analytics Tools / Languages: Spark, SparkR, R, Python, Scala, Hive, SQL, SAS, Tableau, SPSS, Hadoop, Stata, Google Analytics, Amazon Web Services