Sr. Data Scientist Job Opportunity - The Hershey Company

Job Location: Hershey, PA
Work Arrangement: Hybrid

Job Summary

Join our Procurement Data Science organization at The Hershey Company and drive value through disruptive predictive and prescriptive solutions within our Global Procurement team. The Sr. Data Scientist will collaborate with Hershey business partners, technical engineers, data architects, and project managers to ensure adherence to data science standards, delivering rapid and impactful benefits.

As a Sr. Data Scientist, you'll act as a trusted advisor to our business partners, crafting data-driven, machine-intelligent capabilities that directly influence our business. You'll work with large datasets, recommend cutting-edge technologies, and use advanced methods to tackle real business problems. Your role involves designing and running experiments, researching new algorithms, and optimizing risk, profitability, and customer experience. You'll collaborate with a diverse team of business analysts, technical engineers, data architects, and project managers to align outcomes with our business 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 must have a strong background in machine learning, statistical modeling, feature discovery/selection, optimization, exploratory data analysis, data mining, and pattern recognition.

Key Responsibilities

Data Science Solution Delivery

Analyze the source-to-pay data landscape to develop the Global Procurement Data & Technology roadmap using best practice data science methods, frameworks, and governance processes.

Data Science Domain

Collaborate with IT and business partners to define, manage, and deliver innovative data science solutions that drive growth and capability adoption at Hershey.

Data Science Advocacy

Evangelize future data science solutions identified by Enterprise Data leadership. This includes innovations like machine learning, statistical modeling, feature discovery/selection, optimization, exploratory data analysis, data mining, and pattern recognition.

Lead and coordinate key cross-functional data science efforts to accelerate value creation for agile execution team outcomes through machine learning. Work as part of a team using machine learning (ML), deep learning (DL), and other analytical techniques to develop scalable solutions for business problems. Interact with business partners, technologists, and engineers to define and understand business problems, helping to implement ML/DL algorithms where appropriate. Design, develop, and evaluate innovative models for predictive learning, content ranking, and anomaly detection. Extract relevant information from historical data to automate and optimize key processes. Collaborate closely with technology, business, and engineering teams to implement models and adopt new algorithms.

Oversee the health and evolution of agile execution team data science technologies using best practice guidance from the Enterprise Data team. Maintain a holistic vision with a strategic focus on automating existing processes to drive key business performance. Articulate the business benefits of data science while maintaining relationships with business analysts, data architects, technical engineers, and project managers. Routinely meet with the entire execution team (business analysts, data scientists, technical engineers, data architects, project managers), business stakeholders, and the Director of Data Science. Occasionally meet with senior technology leaders and key business partners.

Minimum Requirements

Knowledge, Skills, and Abilities:

• Working knowledge of agile frameworks
• Ability to manage multiple priorities, meet deadlines, and produce quality results under pressure
• Demonstrated leadership and managerial skills
• Strong problem-solving and analytical skills
• Strong team player, change agent, and advocate
• Excellent customer service skills
• High-energy self-starter
• Excellent verbal and written communication skills

Education:

Bachelor’s degree in a STEM field required.
PhD preferred.

Experience:

• 3+ years of professional experience applying quantitative research to optimize human decisions using technologies like machine learning and/or deep learning
• 2+ years using major machine learning/deep learning frameworks (e.g., Scikit-learn, PyTorch, TensorFlow, Keras) and algorithms (e.g., CNN, GAN, LSTM, RNN, XGBOOST)
• 2+ years of data engineering experience with modern big data analytics architectures (Hadoop, SQL, HIVE, Spark, etc.) on major cloud platforms (e.g., AWS, Azure