> Markdown version of [/jobs/ext/2113134-data-scientist-iii](https://www.wearedevelopers.com/jobs/ext/2113134-data-scientist-iii). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist III - **Company:** United States Steel Corporation - **Location:** Pittsburgh, PA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Analysis of Variance (ANOVA), Microsoft Azure, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Oracle (Applications), Power BI, OneStream, Backtesting, Tensorflow, SAP (Applications), SQL Databases, Tableau (Software), Enterprise Data Management, GitHub Copilot, Pytorch, Large Language Models, Snowflake, Scikit Learn, Information Technology, Performance Monitor, Feature Selection, Data Management, Machine Learning Operations, Data Pipelines, Databricks - **Published:** August 19, 2026 - **Apply:** https://www.juju.com/job/00000000gnqk0p ## About the Role + 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, PyTorch, TensorFlow, MLflow, LangChain, 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 OneStream, 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. ## Description + 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. ## Related Videos - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Industrializing your Data Science capabilities](https://www.wearedevelopers.com/videos/178-industrializing-your-data-science-capabilities) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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