Data Science Analyst

Duties & Responsibilities

• Develop, validate, and deploy statistical and machine learning models to improve forecasting accuracy across revenue and expense categories in a complex Utilities & Energy business environment.
• Build driver‑based and predictive forecasting models to support annual planning, rolling forecasts, and long‑range planning, with an accountable focus on measurable improvement.
• Perform model validation, back‑testing, and error analysis to continuously improve performance and ensure trusted, decision‑ready outputs.
• Translate analytical results into clear financial narratives and actionable recommendations for FP&A leadership and business partners, demonstrating strong business acumen and customer focus.

• Champion data integrity by supporting initiatives that improve the quality, consistency, and reliability of financial and operational datasets.
• Define and apply data validation, reconciliation, and control frameworks across key FP&A data flows to ensure accuracy and accountability.
• Collaborate closely with IT and Data Engineering partners to ensure accurate integration of ERP, planning, and operational systems; clear data lineage; and standardized definitions that enable a single source of truth.
• Proactively identify and remediate data issues that impact forecasting, reporting, and downstream decision‑making in an agile and solution‑oriented manner.

• Conduct deep‑dive analyses to uncover trends, risks, opportunities, and key business drivers that inform strategic and operational decisions.
• Deliver executive‑ready insights, visualizations, and storytelling that enable leaders to act with confidence.
• Design, maintain, and evolve standardized KPIs and metrics frameworks aligned to business objectives and customer outcomes.
• Enable self‑service analytics by creating curated datasets, reusable logic, and scalable analytical solutions that drive innovation and continuous improvement.

Required Skills & Experience

• Bachelor’s degree in Data Science, Statistics, Finance, Accounting, Economics, Computer Science, or a related field.
• 3+ years of experience in data analytics, data science, or FP&A, with demonstrated exposure to advanced analytics or predictive modeling.
• Strong proficiency in Python and/or R for statistical analysis and machine learning in a business context.
• Strong proficiency in SQL for data extraction, transformation, validation, and quality assurance.
• Experience building, evaluating, and interpreting predictive or statistical models that support real‑world business decisions.
• Working knowledge of forecasting concepts, financial drivers, and data governance principles, with a strong data integrity mindset.
• Ability to clearly and authentically communicate complex analytical concepts to non‑technical stakeholders and senior leaders.

Preferred Skills & Experience

• Experience in FP&A, corporate finance, or financial analytics within a large, matrixed organization.
• Familiarity with enterprise financial systems such as Oracle ERP/EPM, SAP, or similar platforms.
• Experience working with modern data platforms such as Microsoft Fabric, Snowflake, Databricks, or comparable technologies.
• Experience using Power BI or other business intelligence tools to deliver clear, customer‑centric visualizations and insights.
• Exposure to model lifecycle concepts including deployment, monitoring, and continuous improvement.
• Experience operating in large, multi‑source data environments, demonstrating collaboration, agility, and innovation.

The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills and experience and may vary by location. The base salary is $75,000-130,000 annually.

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