Sr. Data Scientist (Machine Learning Engineering)

Key Skills

Amazon Simple Storage Service (S3), Apache Airflow, AWS, Business Analytics, Data science techniques, Docker, DynamoDB, ETL frameworks, Google Cloud Platform (GCP), Kubernetes-K8s, Machine learning techniques, NoSQL, PLSQL, Python Programming, R Programming, SQL, Statistics

Job Description

Summary of Duties: As a member of Enterprise Analytics and Data Science team, lead projects to build company’s Omnichannel experience by working on machine learning models in areas of customer, pricing, inventory, space and assortment to help discover insights that drive customer, operational and product strategy for Stores, eCommerce and Services. Use customer, operational and interaction data to build datasets that can be consumed by various models and reports. Perform data modeling, exploratory data analysis, and feature selection using advanced statistical methods and techniques. Calibrate models to provide interpretable and repeatable outputs by using various metrics like ROC, RMSE, quantile loss, etc. Model output will be consumed by various teams to create best in industry customer experience. Clearly document objective, statistical model, and results.

Qualifications: Master’s degree or equivalent in Statistics, Mathematics, Economics or related field of study and 4 years of combined experience in Data Science and Business Analytics. Employer will accept a Bachelor’s degree or equivalent in Statistics, Mathematics, Economics or related field of study and 8 years of combined progressive experience in Data Science and Business Analytics in lieu of a Master’s degree or equivalent and 4 year of experience.

Applicants must have demonstrated experience with the following:

analysis of large, complex, multi-dimensional data sets with variety of tools; data mining, machine learning and statistical learning techniques; quantitative analysis techniques to include: clustering, regression, pattern recognition, or descriptive and inferential statistics; time series forecasting; design of experiments and hypothesis testing, drive causal conclusions; statistical programming and modeling in R and Python; ETL (Extract, Transform, Load) processes, relational database (SQL or PL*SQL), and NoSQL database; large scale data processing infrastructures using distributed systems (including Hadoop, Hive, and Spark); providing data visualizations using data visualization tools; cloud-based technology for machine learning model development and deployment using Amazon Web Services (AWS) or Google Cloud Platform (GCP), to include: cloud-based data processing and storage infrastructures (Amazon DynamoDB, Amazon S3); cloud-based machine learning model development using Amazon SageMaker; cloud-based serverless computing service (AWS Lambda); and building and landing data engineering pipelines and workflows using Airflow; Machine Learning model deployment using container orchestration system including Docker and Kubernetes; building and landing machine learning models as web application using Flask Python; building machine learning models using TensorFlow or PyTorch; distributed version control system using Git or GitLab; mathematical optimization, decision analysis, and linear and nonlinear programming; communicating and presenting results of data analyses; and partnering and collaborating with cross-functional teams such as Marketing, Engineering, User Research, and Product.

CONTACT: Individuals interested in applying for the position may email a resume with Job #YC-0929 to valerie.giroux@petco.com.

The pay range(s) below are provided in compliance with state specific laws. Pay ranges may be different in other locations.

$151,736 - $178,900/year. Exact rate of pay will be based on position, location, and experience level.