Description
The Moz Group Data Engineering teams are looking for an experienced Data Scientist with a strong Python background. This position will work from an engineering perspective with both the engineering and product teams to improve data modeling within our services and products. The ideal candidate will have experience using a broad set of statistical and machine learning techniques including supervised / unsupervised data sets and NLP.
Responsibilities
- Comfortable learning Moz's SEO domain, creating appropriate models, and working with engineering and product teams to see those models implemented and released in the context of a standard framework, e.g. Spark or Hadoop
- Comfortable engaging with both engineering and product teams to educate them on data science topics, identify opportunities to apply data science to further our business and operational objectives, and advocate for action based on these identified opportunities
Technologies
- Python
- Cloud providers, e.g. AWS
- Prototyping, e.g. Jupyter Notebook
- Frameworks, e.g. Spark, Hadoop, pandas
Data Science Skills
- Forecasting
- Inferential statistics
- Regression / classification
- NLP, e.g. Word2Vec
About the Moz Group
The Moz Group, a subsidiary of Ziff Davis, Inc (NASDAQ: ZD), is a leading provider of marketing technology solutions primarily for small and medium-sized enterprises that empowers businesses to engage their customers digitally and grow their revenue by leveraging the power of online search, email, voice, and text communications.
About Ziff Davis
At Ziff Davis, we work in a culture of collaboration and welcome those who desire to join our growing global community. We seek enthusiastic individuals with an entrepreneurial spirit looking for and environment that rewards your best work.
Ziff Davis offers competitive salaries, health benefits, and company-sponsored events. If you are seeking a dynamic work environment where you can see the direct impact of your performance, then Ziff Davis is the place for you. We offer a fun and friendly working environment where individuals are given the opportunity to learn, grown and build on their skills.
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