INTL - LATAM - Data Scientist
Role details
Job location
Tech stack
Job description
- Build predictive models using statistical and machine learning techniques used for
classification, regression, and disambiguation. Assess model performance and
communicate with stakeholders.
- Derive insights from data to improve our understanding of user identity and their journey
from online to retail.
- Mine and analyze prescription drug pricing and volume to drive optimization and
improvement of product development; refine attribution and identity through NLP
cleaning and typo correction techniques
-
Support our marketing DS team with audience selection and generation
-
Partner with the rest of the data team to improve data consistency, cleanliness, and
Requirements
GoodRx is looking for a Data Scientist to join our data team. Our ideal candidate is a team
player, a curious, relentless learner who's ready to take on new challenges and deliver value in
a dynamic environment. This individual will have a love of working with data and making
discoveries, a deep understanding of mathematics and statistical modeling, and strong
communication skills. This role will bring rigor to advanced projects across pricing and
identity/attribution data initiatives., * 3+ years of work experience in data science/machine learning;
- Experience delivering impact through statistical analysis and predictive modeling;
domain knowledge in areas of pricing, identity, and attribution preferred.
- Strong knowledge with databases such as RedShift, PostgreSQL, and Spark/EMR;
familiarity with Python computing stack
- Comfortable with ambiguity, adaptable to a high-change environment, and open to new
concepts and processes
-
Any prior exposure to the prescription/healthcare industry is a plus.
-
Undergraduate degree in a quantitative field (e.g. hard sciences, engineering, operations research, etc); advanced degree is a plus.
-
Experience delivering impact through statistical analysis and predictive modeling;domain knowledge in areas of pricing, identity, and attribution preferred.