Entry Level Data Scientist Resume 1
ENTRY-LEVEL DATA SCIENTIST
Entry-Level Data Scientist
tmathers@email.com
(123) 456-7890
Summary
Data enthusiast with a Master’s degree in Data Science and 2 years of experience. Advanced knowledge in machine learning and statistical analysis. Proven track record of designing and implementing data-driven solutions for business improvement.
Experience
Data Scientist Intern
Niantic
04/2020 – 04/2021
Seattle, WA
Developed a program in SAS that automated refinement of linear regression models for specific segments of a customer base that saved 22 hours of labor per month.
Received, cleaned, and prepped data from client using SAS, SQL, and Excel to help data scientists build marketing mix models that resulted in a lift in ROI of 10 basis points.
Statistics and Mathematics Tutor
Seattle University Tutor Center
04/2019 – 04/2020
Seattle, WA
Assessed students’ learning to determine learning weaknesses and needs, successfully helping students perform 13% better in algebra, pre-calculus, calculus, and statistics undergraduate courses.
Met with 30+ students per week through online learning platforms or in a 1:1 setting at the tutor center.
Scheduled weekly statistics and math appointments for students.
Communicated with professors about curriculum, and submitted reports 2 times a week to maintain up-to-date plans for students.
Education
B.S.
Seattle University
09/2017 – 04/2021
Seattle, WA
Skills
- Base SAS
- Clustering
- Data Science
- Data Visualization
- decision trees
- Econometrics
- EXCEL
- Game Theory
- K-Means
- Linear Algebra
- linear regression
- macros
- Mathematics
- MySQL
- Random Forest
- SAS
- SQL
- statistics
- supervised learning
- SVM
- unsupervised learning
Projects
Fantasy Football Models
01/1970 – 01/1970
Aggregated and prepped 3 years of fantasy football projection data from 3 independent sources into a MySQL database.
Created a random forest model in SAS, combining disparate sources into one projection that outperformed the mean absolute error of the next best projection by 15%.
Entertainment Engine
01/1970 – 01/1970
Aggregated data from IMDB and Rotten Tomatoes, and used k-nearest-neighbors in SAS, constructing an enhanced entertainment selection targeted to reach 15- to 25-year-olds.
Improved methodologies to save an average of 12 minutes per movie selection and 3 minutes per song selection.
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