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Remote, Oregon, United States
Remote only
The Predictive Analytics Consultant will be a key member of the analytics team, responsible for leading the design and delivery of critical solutions like Automated Underwriting and Risk Scoring, and Portfolio Monitoring. The position will focus on building, testing, validating, and deploying predictive and optimization models that support credit underwriting decisions in AWS.
The ideal candidate will possess deep expertise in AWS to own the end-to-end deployment and operationalization of machine learning models in production environments. You will architect and implement scalable ML infrastructure leveraging AWS SageMaker, Lambda, and Step Functions to automate model deployment, retraining, and inference pipelines. You will design and deploy comprehensive monitoring solutions using CloudWatch and custom metrics to track model performance, detect data drift, and identify outliers and anomalies in real-time.
Your responsibilities include establishing MLOps best practices and building data quality systems, implement alert mechanisms for performance degradation, and ensure logging and tracing for explainability and compliance. You should be comfortable optimizing costs through resource management and scaling strategies while maintaining enterprise-level reliability, observability, and security. The ideal candidate will have proven experience deploying ML systems at scale, strong proficiency with AWS services, and a passion for transforming research models into production-grade systems.
Responsibilities:
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.
Qualifications:
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
3-5+ years of experience deploying predictive analytics or machine learning models in production environments.
Strong expertise with AWS cloud services, including SageMaker, Lambda, Step Functions, CloudWatch, S3, IAM, and related technologies.
Proficiency in Python and SQL, with experience building scalable data pipelines, model automation, and production-ready analytical solutions.
Hands-on experience implementing MLOps best practices, including CI/CD, model versioning, automated deployment, monitoring, and lifecycle management.
Experience developing and deploying predictive models for credit risk, underwriting, fraud detection, portfolio monitoring, or other financial services applications is preferred.
Experience designing monitoring frameworks for model performance, data quality, data drift detection, anomaly detection, and operational alerting.
Excellent analytical, problem-solving, and communication skills with the ability to translate complex technical concepts into business-focused recommendations.
Proven ability to manage multiple priorities, collaborate across cross-functional teams, and deliver high-quality solutions in a fast-paced, client-focused environment.
Excellent communication skills to present technical concepts to non-technical stakeholders.
Ability to work independently and as part of a team in a fast-paced, dynamic environment.
Strong project management skills with the ability to handle multiple tasks and deadlines.