Position:                       Manager, Data Science

Location:                      Hershey, PA  

Duties: Lead and coordinate key cross-functional data science efforts to accelerate value creation for Hershey through Artificial Intelligence and Machine Learning. Partner with business stakeholders, technologists and engineers to define and understand business problems, and aid them to implement state of the art machine learning and deep learning. Design, develop and evaluate highly innovative models for predictive learning, content ranking, and anomaly. Ensure reliability in data and data pipelines, enforcing governance, security, and performance. Lead, manage, coach, develop and supervise internal and/or external resources to achieve organizational goals. Using best practices, oversee the health and evolution of team engineering technologies. Collaborate with data leaders to build a curriculum for upskilling and conditioning the data science competencies. Partner to deliver a modern data science model that follows Dev/Ops principles and standards for continuous integration / continuous delivery (CI/CD) processes. Collaborate in developing best in class key performance indicators to measure the performance and quality of the data science teams and processes. Able to articulate the holistic benefits of data science from a business perspective, while maintaining the relationship with business analysts, data engineers, technical engineers, and project managers. Hybrid remote work is permitted.

Requirements: a master’s degree in Statistics, Mathematics, Data Science, Computer Science, Information Systems Management, Engineering, or a related field and 2 years of experience in the job offered or in related occupation. Experience must include but need not be maintained over the full term of experience:

1. Experience with applying quantitative research in optimizing human decisions using technologies including machine learning and/or deep learning;
2. Data engineering experience with large-scale data storage processing architectures including: Hadoop, SQL, HIVE, and Spark/SparkR;
3. Experience working with cloud-based analytical systems including AWS, Azure, and Google Cloud; and
4. Experience with major machine learning/deep learning frameworks including Scikit-learn, TensorFlow, and Keras.

No calls/agents/visa sponsorship.