We offer:

• Career Development
• Competitive Compensation and Benefits
• Pay Transparency
• Global Opportunities

Learn More Here: provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race color religion age sex national origin disability status genetics protected veteran status sexual orientation gender identity or expression or any other characteristic protected by federal state or local laws.

This policy applies to all terms and conditions of employment including recruiting hiring placement promotion termination layoff recall transfer leaves of absence compensation and training.

The base pay range for this role is estimated to be $113625 - $174225 at the time of posting. Final compensation will be determined by various factors such as work location education experience knowledge and skills.

Tasks and Qualifications:

What you will do in this role:

AI / ML Solution Development

• 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 LLMpowered 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.

Model Validation & Governance

• Conduct model validation backtesting 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.
• Document model assumptions methodologies limitations and change history for audit and risk review.

ML Ops & Deployment

• Implement MLOps standard methodologies including model versioning and lifecycle management drift detection and performance monitoring retraining schedules and automated pipelines and reproducibility and rollback procedures.
• 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.

Collaboration & Enablement

• Collaborate with Finance IT data teams and other AI workstreams to promote consistent standards tooling and patterns across the organization.
• Serve as a technical thought partner to Finance leaders helping shape the AI roadmap and identify highvalue use cases.
• Help educate Finance partners on AI capabilities limitations and responsible usage.
• Contribute to establishing foundational AI practices for a growing Finance AI team.

What we are looking for:

• Bachelors 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 handson production deployment experience.
• Strong experience building ML models using Python and common libraries (e.g. pandas scikitlearn PyTorch TensorFlow).
• Experience developing or integrating LLMbased solutions (prompt engineering embeddings retrievalaugmented generation summarization).
• Proven understanding of timeseries forecasting anomaly detection regression and classification techniques.
• Experience with model validation backtesting performance monitoring and explain-ability.
• Practical experience implementing MLOps concepts (CI/CD for models monitoring version control).
• Ability to work in a lowmaturity AI environment creating structure where little exists.
• Strong communication skills with the ability to explain technical concepts to Finance and business audiences.

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