We are seeking a Senior Data Scientist, Finance Analytics to join the Finance Transformation team within Corporate FP&A. 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:
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Design, develop, and maintain AI-enabled financial dashboards, analytics applications, and executive reporting tools using Databricks and related analytics platforms.
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Build scalable ETL and ELT data pipelines that integrate financial, operational, and enterprise data into the Enterprise Data Platform.
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Develop and validate machine learning models for financial forecasting, scenario analysis, variance analysis, anomaly detection, and business trend identification.
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Apply generative AI and large language model capabilities to streamline financial reporting, management commentary, knowledge retrieval, and self-service financial analysis.
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Automate recurring FP&A, monthly close, and management reporting processes to reduce manual effort and improve accuracy.
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Partner with Corporate FP&A, segment finance teams, data engineering, and business stakeholders to translate financial questions into scalable analytics and AI solutions.
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Prepare executive-ready analyses, insights, and narratives that support forecasting, financial steering, monthly close activities, and strategic decision-making.
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Own end-to-end delivery of analytics initiatives, including requirements gathering, solution design, model development, testing, deployment, adoption, and ongoing performance monitoring.
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Ensure AI and financial modeling outputs are explainable, auditable, traceable to source data, and aligned with finance governance standards.
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Document model assumptions, data definitions, controls, business rules, and process requirements as analytics capabilities scale.
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Mentor junior team members on data science, financial analytics, responsible AI, and engineering best practices.
Qualifications:
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Bachelor’s degree in Computer Science, Data Science, Engineering, Finance, Economics, Statistics, Mathematics, or a related field; Master’s degree preferred.
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3-5 years of experience in data science, financial analytics, FP&A analytics, or a related quantitative role.
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Advanced proficiency in Python and SQL, including experience writing production-grade, testable code and building complex financial data pipelines.
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Hands-on experience developing, validating, and monitoring machine learning models, with emphasis on forecasting, regression, classification, anomaly detection, or optimization use cases.
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Experience building and validating time-series forecasting models, including back testing, feature selection, model interpretability, and overfit prevention.
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Familiarity with AI and machine learning libraries and frameworks such as Scikit-learn, PyTorch, TensorFlow, MLflow, LangChain, or similar tools.
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Experience applying generative AI or LLM-based solutions to business workflows, financial reporting, knowledge retrieval, commentary generation, or analytics automation preferred.
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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.
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Experience with Power BI, Tableau, or other BI tools, including financial dashboard development and KPI visualization, preferred.
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Exposure to financial systems such as OneStream, Oracle GL/EPM, SAP, ERP, EPM, or similar platforms preferred.
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Strong understanding of financial planning, forecasting, budgeting, variance analysis, cost drivers, profitability analysis, or management reporting preferred.
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Familiarity with LLM-powered code assistants, such as Codex, GitHub Copilot, Claude Code, or similar tools.
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Ability to explain complex models and analytical outputs to Finance leaders and non-technical stakeholders in a clear, practical, and business-relevant manner.
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Manufacturing, industrial, or capital-intensive industry experience a plus.
