Design, develop, and deploy machine learning models and AI solutions tailored to Finance use cases (e.g., forecasting, planning, variance analysis, anomaly and risk detection).
Build and maintain ML models for financial planning, forecasting, trend analysis, and anomaly detection across large, structured datasets.
Develop LLM‑powered tools to support financial analysis, commentary generation, summarization, and scripted insights for Finance users.
Translate Finance requirements into data pipelines, feature engineering, model architecture, and deployment approaches.
Conduct model validation, back‑testing, and performance evaluation to ensure accuracy, robustness, and business relevance.
Evaluate model performance over time and diagnose issues related to data quality, concept drift, and changing business conditions.
Implement appropriate controls, explain-ability, and documentation to support Finance governance, audit, and compliance requirements.
Partner with IT to deploy models into enterprise environments (cloud, Salesforce, SAP, Snowflake, proprietary tools, etc.).
Ensure AI solutions are secure, scalable, and maintainable within enterprise standards.
Requirements
Bachelor’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, Finance, or a related field.
4-7+ years of proven experience in machine learning, data science, or applied AI with hands‑on production deployment experience.
Strong experience building ML models using Python and common libraries (e.g., pandas, scikit‑learn, PyTorch, TensorFlow).