Data Scientist III

We are seeking a Senior Data Scientist, Finance Analytics to join the Finance Transformation team within Corporate FP&A. It is a hybrid position in Pittsburgh, PA. This role will design, build, and scale AI-enabled analytics solutions that modernize financial planning, forecasting, reporting, and decision support across USS.

The ideal candidate brings a strong combination of finance acumen, advanced analytics, machine learning, automation, and applied generative AI experience. This is a high-impact opportunity to work at the intersection of Finance, data engineering, and AI to improve forecast accuracy, reduce manual effort, strengthen financial insights, and enable faster, more confident decision-making for senior leadership.

Responsibilities

Design, develop, and maintain AI-enabled financial dashboards, analytics applications, and executive reporting tools using Databricks and related analytics platforms. Build scalable ETL and ELT data pipelines that integrate financial, operational, and enterprise data into the Enterprise Data Platform. Develop and validate machine learning models for financial forecasting, scenario analysis, variance analysis, anomaly detection, and business trend identification.

Apply generative AI and large language model capabilities to streamline financial reporting, management commentary, knowledge retrieval, and self-service financial analysis. Automate recurring FP&A, monthly close, and management reporting processes to reduce manual effort and improve accuracy. Partner with Corporate FP&A, segment finance teams, data engineering, and business stakeholders to translate financial questions into scalable analytics and AI solutions.

Prepare executive-ready analyses, insights, and narratives that support forecasting, financial steering, monthly close activities, and strategic decision-making. Own end-to-end delivery of analytics initiatives, including requirements gathering, solution design, model development, testing, deployment, adoption, and ongoing performance monitoring.

Ensure AI and financial modeling outputs are explainable, auditable, traceable to source data, and aligned with finance governance standards. Document model assumptions, data definitions, controls, business rules, and process requirements as analytics capabilities scale. Mentor junior team members on data science, financial analytics, responsible AI, and engineering best practices.

Qualifications

Bachelor’s degree in Computer Science, Data Science, Engineering, Finance, Economics, Statistics, Mathematics, or a related field; Master’s degree preferred. 3-5 years of experience in data science, financial analytics, FP&A analytics, or a related quantitative role. Advanced proficiency in Python and SQL, including experience writing production-grade, testable code and building complex financial data pipelines.

Hands-on experience developing, validating, and monitoring machine learning models, with emphasis on forecasting, regression, classification, anomaly detection, or optimization use cases. Experience building and validating time-series forecasting models, including back testing, feature selection, model interpretability, and overfit prevention.

Familiarity with AI and machine learning libraries and frameworks such as Scikit-learn, Py. Torch, Tensor. Flow, MLflow, Lang. Chain, or similar tools. Experience applying generative AI or LLM-based solutions to business workflows, financial reporting, knowledge retrieval, commentary generation, or analytics automation preferred.

Hands-on experience with a modern cloud-based analytics platform such as Databricks or Snowflake and a major cloud provider such as Azure, AWS, or GCP. Experience with Power BI, Tableau, or other BI tools, including financial dashboard development and KPI visualization, preferred. Exposure to financial systems such as One.

Stream, Oracle GL/EPM, SAP, ERP, EPM, or similar platforms preferred. Strong understanding of financial planning, forecasting, budgeting, variance analysis, cost drivers, profitability analysis, or management reporting preferred. Familiarity with LLM-powered code assistants, such as Codex, GitHub Copilot, Claude Code, or similar tools.

Ability to explain complex models and analytical outputs to Finance leaders and non-technical stakeholders in a clear, practical, and business-relevant manner. Manufacturing, industrial, or capital-intensive industry experience a plus.