Predictive Analytics Consultant

About the role

You will lead the design and delivery of critical predictive analytics solutions such as Automated Underwriting and Risk Scoring, and Portfolio Monitoring for financial services clients. In this role, you own the full lifecycle of model development, from initial design through testing, validation, and production deployment within the AWS cloud environment. You will translate complex analytical research into robust, production-grade systems that directly support credit underwriting decisions and business objectives. The position requires deep ownership of MLOps infrastructure, ensuring that machine learning models operate reliably, securely, and efficiently at enterprise scale. You will act as a technical leader, bridging the gap between data science innovation and operational implementation for high-impact financial applications. This role demands a strong commitment to quality, security, and compliance within highly regulated financial services contexts. You will continuously optimize solutions to balance performance, reliability, and cost-effectiveness in cloud-based environments.

Key facts

What you'll do

• Lead the design, development, and deployment of predictive analytics solutions, including automated underwriting, risk scoring, portfolio monitoring, and decision optimization models.
• Build, test, validate, and maintain predictive and machine learning models to support credit underwriting, risk management, and portfolio performance objectives.
• Architect, deploy, and manage end-to-end MLOps pipelines in AWS using services such as SageMaker, Lambda, Step Functions, and other cloud-native technologies.
• Develop scalable and automated workflows for model training, deployment, retraining, and inference to ensure efficient and reliable production operations.
• Design and implement comprehensive model monitoring frameworks to track model performance, detect data drift, identify anomalies, and ensure continued model accuracy.
• Build and maintain data quality validation processes that verify data integrity, identify inconsistencies, and support reliable model performance.
• Establish monitoring, logging, tracing, and alerting capabilities that provide visibility into production systems and enable rapid identification and resolution of issues.
• Develop and enforce MLOps best practices, including model governance, version control, CI/CD automation, documentation, and lifecycle management.
• Optimize AWS infrastructure for performance, scalability, security, reliability, and cost efficiency while ensuring enterprise-grade production standards.
• Partner with data scientists, software engineers, product teams, and business stakeholders to translate analytical solutions into production-ready applications.
• Conduct model validation, performance testing, and ongoing maintenance to ensure predictive models remain accurate, compliant, and aligned with business objectives.
• Research, evaluate, and implement new machine learning technologies, cloud services, and analytical methodologies to continuously improve predictive capabilities and operational efficiency.

Requirements

• Hold a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Bring 3-5+ years of experience deploying predictive analytics or machine learning models in production environments.
• Demonstrate strong expertise with AWS cloud services, including SageMaker, Lambda, Step Functions, CloudWatch, S3, IAM, and related technologies.
• Show proficiency in Python and SQL, with experience building scalable data pipelines, model automation, and production-ready analytical solutions.
• Have hands-on experience implementing MLOps best practices, including CI/CD, model versioning, automated deployment, monitoring, and lifecycle management.
• Possess experience developing and deploying predictive models for credit risk, underwriting, fraud detection, portfolio monitoring, or other financial services applications.
• Show experience designing monitoring frameworks for model performance, data quality, data drift detection, anomaly detection, and operational alerting.
• Exhibit excellent analytical, problem-solving, and communication skills with the ability to translate complex technical concepts into business-focused recommendations.
• Demonstrate proven ability to manage multiple priorities, collaborate across cross-functional teams, and deliver high-quality solutions in a fast-paced, client-focused environment.
• Maintain excellent communication skills to present technical concepts to non-technical stakeholders clearly and effectively.
• Demonstrate a strong commitment to maintaining the security, integrity, and compliance of data and models within regulated financial environments.
• Show willingness to collaborate closely with stakeholders to gather requirements, clarify needs, and ensure solutions meet business and technical expectations.

Practical notes

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