JOB SUMMARY:

The Data Scientist Principal role will provide the advanced analytics that lead to improved business profitability, increased operation efficiencies, and higher customer satisfaction. This role requires a combination of conceptual thinking, analytical expertise, research design, business acumen, a strategic mindset, customer focus, and is responsible for modeling complex business problems and discovering business insights using quantitative disciplines and visualization techniques. The successful candidate must possess a fervent desire to work with cross-functional teams while expanding CS&S analytic capabilities to facilitate/affect positive change within the organization.

JOB REQUIREMENTS:

• A bachelor's degree in Data Analytics, Statistics, Computer Science, Econometrics, or a related field is required.
• A master's degree in Data Analytics, Statistics, Computer Science, Econometrics or a related field is highly preferred
• 5+ years of hands-on experience with data and analytics; with progressively increased level of responsibility
• Knowledge of internal and external data, systems, and sources
• Proficient with one or more programming languages (SQL, SAS, R, Python)
• Working knowledge of industry accepted predictive modeling techniques (e.g. Decision Trees, Neutral Nets, and Linear Regression) coupled with the ability to accurately interpret and present the results
• Working knowledge of Hadoop, big data platforms, and data lakes a plus
• Demonstrated experience applying data science methods to real-world data problems
• Knowledge of model implementation a plus
• Experience developing and utilizing visualization tools
• Demonstrated verbal and written communication skills with experience credibly influencing highly technical and executive audiences; the ability to explain data science processes and outcomes in practical terms
• Experience managing and prioritizing multiple projects simultaneously involving internal and external stakeholders
• Ability to effectively collaborate in highly cross-functional environments, engage diverse stakeholders, and translate significant information and varied points of view into insights for Customer Services & Solutions

MAJOR JOB RESPONSIBILITIES:

• Identify and gather data, build and deploy analytical models, and provide data-driven recommendations that enable Customer Services & Solutions to improve services and programs
• Identify solutions for data infrastructure, including data storage, retrieval, governance, and cataloging
• Gather and analyze market data to understand customer trends and expectations and make recommendations to partner organizations to increase satisfaction and grow sales
• Produce analytics to support proactive and preemptive communications
• Enable personalization with analysis of customer preferences and behaviors
• Conduct exploratory data analysis with the goal of recommending initiatives to reduce call volume, reduce customer effort, increase revenues and profitability
• Understand how our customers interact with our company across all our channels
• Analyze structured (quantitative) and unstructured (text, voice recordings) data to develop recommendations to improve customer service performance, optimize channels and programs, and grow profitability
• Produce advanced analytics for business cases
• Assist the Workforce Strategy team with analyzing employee demographics, performance and quality monitoring information and other data to inform both the training content as well as long term workforce plan
• Assist with the Customer Solutions product portfolio analysis
• Assist with the annual Customer Solutions goal setting process including modeling and forecasting
• Establish external and internal relationships to share best practices in data science, stay abreast of industry trends and emerging technologies
• Communicate technical information and complex data to non-technical audiences in a logical and concise manner
• Document success stories of data science making material impact on Customer Services & Solutions business
• Spearhead data literacy within Customer Strategy & Solutions organization; create change management plans to promote data science; build momentum for analytics being critical to the cultural DNA
• Examples of projects:
  • Solutions Portfolio Optimization:
    • Predictive analytics to forecast future outcomes of product success (e.g. demand, customer preferences, opportunities for innovation and enhancement)
    • Lifetime Value Analysis: Estimating future value of a customer over the relationship with the company.
    • Churn Prediction: Identifying customers at risk of unenrolling.
    • Propensity Models: to identify those segments of customers likely to enroll.
    • Satisfaction: Identify satisfaction levels of products
  • Data Ingestion for Cloud Data Platform, which is the new data lake that is rolling as a part of the CIS platform upgrade (ASCEND).
    • This data lake needs to be ingested with various data sources and the principal would be assigned to document Customer Solutions data sources (Oracle Sales Cloud, VisionDSM, LAMP, PSCS, Energy Direct, etc) and ensure they are ingested and curated by the Data Management Hub team.
  • Customer Choice: collaborate on the Load Prediction/Optimization Model
    • Play an active role in supporting leaders in optimizing how to predict load impacts based off customer choice offerings.
  • Integrated Resource Plan & Achieve Customer Program goals:
    • Data ingesting, integration, automation, and dashboard development for programs that will be evaluated as part of the IRP (Energy Efficiency, Renewables, ET, DER, etc). Currently, there are a lot of manual reports being tracked in Excel for programs.
  • Credit & Collections:
    • Arrears Modeling - Build models to predict likelihood of customers to go into arrears and then subsequently into a charge-off state.