Predictive Analytics Consultant
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Job Description
Predictive analytics consultant deploying AWS machine-learning models for MeridianLink’s credit decisioning solutions. Building MLOps pipelines, monitoring systems, and production-grade underwriting and risk-scoring applications.
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 supporting credit underwriting, risk management, and portfolio performance.
- Architect, deploy, and manage end-to-end MLOps pipelines in AWS using SageMaker, Lambda, Step Functions, and other cloud-native technologies.
- Develop automated workflows for model training, deployment, retraining, and inference.
- Design and implement model monitoring frameworks for performance, data drift, anomalies, and accuracy.
- Build and maintain data quality validation processes.
- Establish monitoring, logging, tracing, and alerting capabilities for production systems.
- 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.
- 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.
- Research, evaluate, and implement new machine learning technologies, cloud services, and analytical methodologies.
Requirements:
- 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.
- 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 designing monitoring frameworks for model performance, data quality, data drift detection, anomaly detection, and operational alerting.
- Excellent analytical, problem-solving, and communication skills.
- Ability to translate complex technical concepts into business-focused recommendations.
- Proven ability to manage multiple priorities and collaborate across cross-functional teams.
- Ability to deliver high-quality solutions in a fast-paced, client-focused environment.
- Ability to work independently and as part of a team.
- Strong project management skills with the ability to handle multiple tasks and deadlines.
- Background in credit risk, underwriting, fraud detection, portfolio monitoring, or other financial services applications is preferred.
Benefits:
- Insurance coverage (medical, dental, vision, life, and disability)
- Flexible paid time off
- Paid holidays
- 401(k) plan with company match
- Remote work
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