The Data Science & Analytics group at Penguin Random House is seeking a Data Scientist.
We are an agile team of data scientists and software engineers. The team has a wide mandate encompassing pricing systems, recommendation / personalization systems, title segmentation, supply chain, as well as ad-hoc analysis and data exploration.
In this role, you will have an opportunity to work on a variety of high-profile projects under the mentorship of Senior Data Scientists and in collaboration with key decision makers across the organization.
Please include with your application a link to your GitHub (Bitbucket) repository for a code sample, whether it was for a Kaggle attempt, a school project, or a general open-source contribution. Standalone code samples will also be accepted.
Apply if you have:
- A bachelor’s degree in mathematics, statistics, economics, computer science, business analytics, or any quantitative social science
- Relevant coursework applying advanced statistical/machine learning and predictive analysis techniques
- Intuition for mapping real word problems to relevant analytical methods, models, approaches
- Solid capability in SQL for tasks such as computing aggregates and joining multiple tables
- Expertise in at least one scientific computing / scripting tool, such as R or Python
- A strong, documented desire to rapidly and continually advance skills through on-the-job and off-the-job training (e.g. via MOOCs)
- 2 years of professional experience in a data science role
- Experience working with Python packages such as scikit-learn, pandas, or TensorFlow
- Alternatively, a good understanding of R packages such as ggplot2, rCharts, ri, dplyr, data.table, cvTools, (b)lmer, arm, lasso/glmnet, BayesTree and reshape2/tidyr
- Experience with Stan or other general-purpose modeling tools
- Experience extracting data from APIs
- Experience with UX design and data visualization
- Experience building data products from the warehouse ingestion phase all the way through to the business-facing application side
- Experience with automated feature engineering and large datasets (>1TB)