We're looking for a Data Scientist to join our Analytics team in Pittsburgh, PA, New York, NY or San Francisco, CA! You will leverage advanced analytic and machine learning techniques to solve complex business challenges across marketing and merchandising teams. Out ideal candidate is adept at using large datasets across various data tools to develop and deploy predictive/prescriptive models to gain insights across a multifaceted business. This person must be passionate for discovering solutions hidden in large data sets and working with a wide range of stakeholders and functional teams to improve business outcomes.
- Use predictive modeling to increase and optimize sales and revenue and personalize the customer experience.
- Mine and analyze data from internal databases to drive optimization and improvement of product development, marketing techniques and business strategies.
- Develop statistically significant Test and Learn programs to tests hypothesis and model output.
- Work across business units to identify and understand the right approach to solve business problems.
- Develop processes and tools to monitor a model’s performance.
- Master’s degree in Statistics, Machine Learning, Data Science or another quantitative field major
- 3+ Years experience with a Big Data Environment, preferably Azure.
- 3+ Years experience using web services: Redshift, S3, Spark, etc.
- Strong Organization, organizational agility, technical saavvy, customer Focus, highly analytical, change management skills
- Strong working knowledge of machine learning including, but not limited to regressions, decision trees, neural networks, gradient boosting, etc..
- Highly proficient in Python or R
- Experience working with relational databases and SQL
- Ability to communicate complex findings and insights across the organization.
- Demonstrated ability in working with large, complex datasets across various sources.
- Experience with PowerBI or R Shiny is a plus
- Retail experience, particularly leveraging transactional data, is a plus but not required