Job Location: Hershey, PA
This position is open to Hybrid work.
Summary:
The Procurement Data Science organization drives value for Hershey by creating disruptive predictive and prescriptive solutions across the Global Procurement organization for analytics and decision-making.
The Sr. Data Scientist will be working with Hershey business partners, technical engineers, data architects, and project managers to ensure data science standards adhere to company best practice and help to deliver rapid impactful benefits.
The Sr. Data Scientist 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, recommend 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.
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 to drive growth and adoption of capabilities at Hershey
Data Science Advocacy:
Evangelize future data science solutions identified by Enterprise Data leadership, including innovations such as: 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 outcome delivery through machine learning
- As part of a team, use 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
- Design, develop and evaluate highly innovative models for predictive learning, content ranking, and anomaly detection
- Analyze and extract relevant information from historical data to help automate and optimize key processes
- 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
- 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
- Routinely meets with entire execution team (business analysts, data scientists, technical engineers, data architects, and project managers), business stakeholders and the Director for Data Science. Occasionally meets with senior Technology leaders and with key business partners.
Minimum knowledge, skills and abilities required to successfully perform major duties/responsibilities:
- 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
Minimum Education:
- Bachelors in a STEM degree
- PhD. preferred
Minimum Experience Requirements:
- 3+ years of professional experience with applying quantitative research in optimizing 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 and 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, Google Cloud)
- 2+ years programming skill in Python
- Working knowledge with modern cloud-based data storage and compute environments (e.g., Snowflake, Databricks in Azure, AI-platform in GCP, etc.)
- Experience deploying ML models into discovery/production environment to drive insights using MLOps a plus
- Experience working in an agile delivery model a plus
- Demonstrated leadership and self-direction. Willingness to both teach others and learn new techniques
- Excellent communication and presentation skills, with the ability to articulate new and complex ideas and concepts to technical and non-technical partners
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