Note: The job is a remote job and is open to candidates in USA. MeridianLink is a company focused on providing innovative analytics solutions, and they are seeking a Predictive Analytics Consultant. This role involves leading the design and delivery of predictive models and MLOps pipelines in AWS to support credit underwriting and risk management.
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
Skills
- 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 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
- Experience developing and deploying predictive models for credit risk, underwriting, fraud detection, portfolio monitoring, or other financial services applications
Company Overview
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