Data Scientist

Summary

The Enterprise Data Science organization drives value creation for Hershey through development and deployment of innovative and scalable AI & ML solutions. The Data Scientist plays a key role in Hershey’s enterprise data science roadmap, partnering with Hershey business and technology stakeholders to strategize and execute companywide AI & ML initiatives. You will act as a trusted advisor for Hershey business partners to build new, data-driven, machine-intelligent capabilities that impact the business. You will work with large volumes of data, innovative technologies, and advanced methods to solve real business problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and the customer experience. You will work with a diverse team of business analysts, technical engineers, data architects, and project managers to deliver outcomes aligned with our business partner’s strategy. In addition, you will work directly with the Director of Data Science to ensure consistency and compliance of deliverables to frameworks and governance processes.

Applicants should have a strong background in machine learning, statistical modeling, feature discovery/selection, optimization, exploratory data analysis, data mining and pattern recognition.

Specific Job Responsibilities:

• Participate key cross-functional data science efforts to accelerate value creation for agile execution team outcome delivery through machine learning.
• Apply machine learning (ML), deep learning (DL) and other analytical techniques to create scalable solutions for business problems.
• Interact with business partners, technologists and engineers define and understand business problems, help, and aid them to implement ML/DL algorithms when appropriate.
• Work closely with technology, business, and engineering teams to drive model implementations and adoption of new algorithms.
• Using best practice guidance from the Enterprise Data team, oversee the health and evolution of agile execution team data science technologies.
• Consult project leadership to understand projects’ needs/requirements to recommend opportunities and identify gaps to ensure clean and rapid project delivery.
• Strategic thinker with holistic vison, specific focus on automation of existing processes to drive key business performance.
• Able to articulate the holistic benefits of data science from a business perspective, while maintaining the relationship with business analysts, data architects, technical engineers, and project managers

Minimum Education and Experience Requirements:

Education:

• Master’s degree in Statistics, Mathematics, Data Science, Computer Science, Engineering, or a related discipline with a strong focus on use of predictive analytics and optimization.

Experience:

• 1+ years of professional experience with applying quantitative research in optimizing human decisions using technologies like machine learning, deep learning and/or optimization models
• 1+ years of data engineering experience with large-scale data storage processing architectures (Hadoop, SQL, HIVE, Spark/SparkR, etc.)
• 1+ years of experience working with cloud-based analytical systems (e.g., AWS, Azure, Google Cloud)
• Advanced working knowledge and experience with data science and relational/non-relational databases e.g., Teradata, Snowflake, Databricks, Azure Data solutions and Hadoop
• Experience with major machine learning/deep learning frameworks (e.g., Scikit-learn, PyTorch, TensorFlow and Keras)
• Demonstrated leadership and self-direction. Willingness to both teach others and learn new techniques.
• Ability to communicate complex ideas in a clear, precise, and actionable manner.
• Experience working in a high performing agile delivery model, aligning with Scrum Masters, Product Owners, and other execution team members to deliver rapid and impactful solutions that align to business partner strategy.
• Excellent communication and presentation skills, with the ability to articulate new ideas and concepts to technical and non-technical partners.