Data Science Analyst

Itron, Inc.•Liberty Lake, WA

2d•$75,000 - $130,000•Hybrid

About The Position

Itron is seeking a Data Science Analyst to join our Digital Finance organization and help transform how Finance leverages data, analytics, and predictive modeling. This role sits at the intersection of Finance, Data, and Advanced Analytics, with a primary focus on strengthening financial data quality and governance, improving forecast accuracy through statistical and machine learning models, and delivering scalable, customer‑centric insights that support confident decision‑making. You will collaborate closely with Financial Planning & Analysis (FP&A) leaders and IT partners in a modern enterprise data environment to enable more accurate, timely, and actionable financial outcomes.

Requirements

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.

Nice To Haves

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.

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.