Data Scientist Senior

Arizona Public Service generates clean, reliable and affordable energy for 2.7 million Arizonans. Our service territory stretches across the state, from the border town of Douglas to the vistas of the Grand Canyon, from the solar fields of Gila Bend to the ponderosa pines of Payson. As the state’s largest and longest-serving energy provider, our more than 6,000 dedicated employees power our vision of creating a sustainable energy future for Arizona.

 

Since our founding in 1886, APS has demonstrated a strong commitment to our customers in one of the country’s fastest growing states, earning a reputation for customer satisfaction, shareholder value, operational excellence and business integrity.

 

Our present and future success depends on the creative and dedicated people of our company who demonstrate the principles outlined in the APS Promise: Design for Tomorrow, Empower Each Other and Succeed Together.  

Summary

The Data Scientist Senior is responsible for providing new insights from predictive statistical modeling activities, advanced data analysis, and specialized analytical support for business operations. The Data Scientist will work directly with large amounts of structured and unstructured data from different sources to create meaningful analyses, discover insights and identify opportunities for various business units within the organization. He or she should recommend creative and leading approaches trend analyses, what-if analyses, prescriptive and predictive data modeling and provide thought provoking ideas, methods and algorithms to support business decision making. By applying advanced analytics, this role will build mathematical models like Machine Learning, AI, Regression Analysis, supervised and unsupervised modeling techniques to validate findings using experimental and iterative approach.

Works closely with, subject matter experts, project and program managers, architects, data engineers, partners and vendors to turn data into critical information and insights to make sound business decisions. He or she will be able to work effectively independently as well as in a team environment with focus on collaboration and customer satisfaction. The Data Scientist should have creative thinking, strong analytical skills, innovative and curious mindset and excellent organizational and communication skills.

Minimum Requirements

• BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field and minimum 6 years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role
• OR advanced degree and 4 years directly related experience. Possesses a combination of strong analytical and problem-solving skills and programming knowledge, or an equivalent combination of education and experience with demonstrated comparable knowledge and abilities.
• A high level of proficiency in commonly used programming languages and tools like R Programming, Python and SQL.
• Strong communication, presentation and writing skills. Must be able to lead teams in evaluations and implementation of solutions.
• Must be able to work with key internal and external stakeholders and all levels of management.

Preferred Special Skills, Knowledge or Qualifications:

• Masters or Doctorate degrees in related fields.
• Knowledge/experience in utility industry and business functions.
• Certification in Data Science and/or predictive analytics

 

Major Accountabilities

1) Collaboration with customers and partners:
- Consult with stakeholders and subject matter experts to understand business needs and operations, goals and objectives and key drivers for performance.
- Work closely with the business units to complete data analytics efforts. Build and maintain strong working relationships with customers, partners and vendors.

2) Data requirements and preparation:
- Identify available and relevant data and the data sources.
- Collaborate with SMEs, data stewards and architects for data collection, preparation, integration, quality, exploration and retention.
- Gather data, formulate cluster or nodes and establish performance checks on the large data models.
- Design and implementation of solutions including data acquisition, storage, transformation, and analysis

3) Modeling and Deployment:
- Design, develop and deploy innovative models. Provide insights from predictive statistical modeling activities. Test theories by creating models and experimenting with data.
- Design models, algorithms and visualizations that help distill insights from huge volumes of chaotic data.
- Modeling complex problems, discovering insights/identifying opportunities through statistical, algorithmic, mining