Machine Learning Architect

Job Title: Machine Learning Architect
Location: Dallas TX / Pittsburgh PA

Job Type: Full-Time

Job Summary:

We are looking for a highly experienced Machine Learning Architect with a strong background in delivering ML solutions within the banking and financial services domain. The ideal candidate will lead ML model development, deployment, and optimization efforts, working closely with data scientists, engineers, and business stakeholders to deliver scalable and production-grade solutions.

Key Responsibilities:

• Lead end-to-end design, development, and deployment of machine learning models for use cases such as fraud detection, credit scoring, customer segmentation, risk assessment, and recommendation engines.
• Translate complex business problems into scalable ML solutions using advanced analytics and statistical techniques.
• Work with stakeholders across product, data, and engineering teams to align ML initiatives with business objectives.
• Mentor and guide a team of data scientists and ML engineers.
• Ensure model governance, validation, explainability, and compliance with regulatory requirements.
• Conduct rigorous model performance evaluation and lead initiatives for continuous model improvement.
• Implement MLOps practices for model versioning, monitoring, and retraining.
• Collaborate on data strategy, architecture, and data quality improvements.

Required Skills & Qualifications:

• 8+ years of experience in Machine Learning/Data Science, with 2+ years in a leadership role.
• Strong hands-on experience with Python, Scikit-learn, TensorFlow, PyTorch, or similar ML frameworks.
• Proficiency in SQL and working with large-scale structured and unstructured datasets.
• Deep knowledge of ML algorithms, statistical modeling, feature engineering, and model optimization.
• Experience deploying ML models to production environments (preferably using AWS SageMaker, Azure ML, or Google Cloud Platform AI Platform).
• Solid experience in MLOps, version control, and CI/CD practices.
• Exposure to model risk governance, bias testing, and model explainability tools like SHAP or LIME.
• Bachelor's or Master s degree in Computer Science, Data Science, Statistics, or a related field.

Preferred:

• Experience in banking or financial services is mandatory.
• Familiarity with risk models, regulatory requirements (CCAR, Basel, etc.), and compliance guidelines in the banking industry.
• Strong communication and stakeholder management skills.

Thanks

Govardhan

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