Data Scientist - Customer Service Analytics - Akron FirstEnergy Headquarters

FirstEnergy•Akron, OH

About The Position

The Data Scientist on the Customer Analytics and Reporting team transforms data into actionable insights that drive strategic, operational, and customer-focused decision making across Customer Care. The role combines advanced analytics, statistical modeling, business intelligence, and data engineering to identify opportunities, solve complex business problems, and enhance the customer experience. The position partners with business stakeholders, IT, vendors, and leadership to develop analytical solutions, build scalable data assets, and deliver data-driven recommendations. It also supports data architecture, maintains analytics-ready datasets, and ensures adherence to data governance principles and best practices. The position may be located at any FirstEnergy headquarters location. The selected candidate may be offered a position within the same job family at a lower level based on the interview process and the candidate’s qualifications.

Requirements

Bachelor's degree in Data Science, Statistics, Computer Science, MIS, Mathematics, Economics, Engineering, or a related quantitative field, with 5+ years of related experience.
Strong SQL skills, including CTEs, window functions, and working with semi-structured data such as JSON.
Experience using statistical, analytical, or data science techniques to solve business problems.
Experience with modern cloud data platforms such as Databricks, Snowflake, or similar environments.
Experience building reports, dashboards, and visualizations in Power BI, Tableau, or similar BI platforms.
Proficiency in Python, R, or another analytical programming language for data analysis and model development.
Ability to translate business requirements into analytical approaches, data models, and actionable recommendations.
Strong critical thinking, problem-solving, and data storytelling skills.
Strong written and verbal communication skills, with the ability to work directly with non-technical stakeholders.

Nice To Haves

Experience developing predictive models, forecasting solutions, customer segmentation strategies, or machine learning applications.
Familiarity with utility customer systems and integrations, including SAP, CRM platforms, and call center technologies.
Knowledge of customer privacy, security, and regulatory requirements, including experience supporting audits, regulatory filings, or legal data requests.
Understanding of utility industry customer dynamics, including billing, outages, reliability, low-income programs, and the regulatory environment.
Experience with experimentation, performance measurement, and KPI development.
Experience applying data science methodologies to improve customer experience, operational efficiency, or business performance.

Responsibilities

Analyze large and complex datasets to identify trends, customer behaviors, operational opportunities, and business insights.
Develop statistical models, forecasting solutions, segmentation frameworks, and predictive analytics to support customer and business outcomes.
Design, develop, optimize, and maintain data pipelines and transformation logic that turn raw source data into clean, analytics-ready datasets.
Develop and maintain dimensional data models and curated datasets that support Power BI reporting, self-service analytics, and advanced analytical use cases.
Partner with business stakeholders to translate business challenges into analytical solutions and actionable recommendations.
Write and optimize complex SQL queries against large-scale data platforms to support reporting, analysis, and model development.
Build dashboards, visualizations, and analytical tools that communicate findings and enable data-driven decision making.
Evaluate and monitor model performance, data quality, and analytical outputs to ensure reliability, accuracy, and business value.
Establish and promote data governance standards, including documentation, data definitions, naming conventions, and version control practices.
Collaborate with IT and business partners to identify opportunities for automation, process improvement, and scalable data solutions.
Present insights, findings, and recommendations to leaders and non-technical audiences in a clear and concise manner.
Support regulatory, operational, and strategic initiatives through data analysis, reporting, and analytical research.