Data Science and Analytics Engineer

This role will focus on developing production-grade AI, machine learning, and analytics platforms that support critical business functions including risk management, fraud detection, compliance, customer intelligence, and operational optimization. The ideal candidate combines deep technical expertise with the ability to drive business outcomes through data-driven innovation.

This role will focus on developing production-grade AI, machine learning, and analytics platforms that support critical business functions including risk management, fraud detection, compliance, customer intelligence, and operational optimization. The ideal candidate combines deep technical expertise with the ability to drive business outcomes through data-driven innovation.

What You'll Tackle:

• Design and deploy enterprise AI and machine learning solutions.
• Build scalable analytics pipelines using distributed computing frameworks.
• Develop predictive models, forecasting solutions, and advanced analytics capabilities.
• Implement MLOps frameworks and automated model deployment pipelines.
• Partner with engineering teams to integrate models into enterprise applications.
• Lead model monitoring, governance, validation, and explainability initiatives.
• Drive AI adoption across business processes and decision workflows.
• Collaborate with technology, risk, compliance, and business stakeholders.

QUALIFICATIONS

• 10+ years of experience in data science, machine learning, AI engineering, or quantitative analytics.
• Advanced expertise in Python and SQL.
• Strong experience with Databricks and Apache Spark.
• Experience with Azure ML and cloud-based analytics platforms.
• Hands-on expertise with TensorFlow, PyTorch, scikit-learn, XGBoost, and MLflow.
• Experience deploying production-grade machine learning solutions.
• Strong understanding of MLOps, CI/CD automation, and model lifecycle management.
• Experience operating within regulated industries.
• Strong communication and stakeholder management skills.
• Bachelor's degree in a quantitative discipline.
• Banking or financial services experience.
• Fraud, AML/BSA, or risk analytics experience.
• Generative AI and LLM implementation experience.
• NLP and intelligent automation experience.
• Real-time inference architecture experience.
• Model risk management expertise.
• Master's degree or PhD.