Senior Engineer, Data Science – Machine Learning Operations

This is a fully remote position, open to applicants in Tennessee.

📋 Description

• Collaborate directly with business stakeholders to uncover opportunities for leveraging data and machine learning to enhance decision-making, efficiency, or outcomes.

• Develop experiments and hypotheses that can be swiftly validated using accessible data and practical modeling techniques.

• Manage models throughout their lifecycle—from data preparation and feature engineering to deployment, monitoring, and iterative improvements based on real-world results.

• Deploy machine learning models into production utilizing AWS-native tools and integrate them into operational workflows and downstream systems.

• Implement machine learning training and inference pipelines on Amazon SageMaker, which include pipelines, endpoints, model registry, and monitoring.

• Monitor model performance (accuracy, drift, stability, business KPIs) and make adjustments based on real-world effects.

• Construct and manage data ingestion and transformation pipelines across both batch and event-driven workloads using AWS Glue, zero-ETL integrations, Step Functions, EventBridge, and related services.

• Work closely with IT, Security, and Platform Engineering teams to ensure alignment with enterprise security, compliance, and operational standards.

• Utilize infrastructure as code (Terraform, CDK, or CloudFormation) to create repeatable and scalable environments.

• Oversee and manage S3-based data lake infrastructure, including Iceberg table formats, AWS Glue Data Catalog, and AWS Lake Formation.

• Implement and uphold data zone architecture (e.g., raw, curated, and consumption zones) to facilitate governed data access and lifecycle management.

• Define and establish data access controls using Lake Formation permissions and IAM-aligned policies.

• Create and maintain data governance practices, including schema management, schema evolution, and lineage tracking.

• Ensure data assets are discoverable, auditable, and secure through cataloging, metadata management, and access controls.

• Develop end-to-end observability using CloudWatch, Datadog, pipeline SLAs, data quality checks, and model drift detection.

• Establish operational runbooks and support procedures for governed data and machine learning platforms.

• Apply cost-aware design principles when selecting data processing, training, and inference strategies.

• Optimize Glue, SageMaker, and storage utilization to provide value efficiently at scale.

• Continuously enhance platform reliability, scalability, and cost-effectiveness as data and machine learning workloads expand.

⛳️ Requirements

• Over 5 years of experience in a professional data science role.

• 5 years of experience with machine learning pipelines, preferably within an AWS environment.

• A problem-solver focused on delivering business outcomes and actionable results.

• Strong collaborator capable of translating inquiries into testable hypotheses and actionable solutions.

• Hands-on experience in applying machine learning and delivering models to production in AWS settings.

• Demonstrated experience in managing governed data lakes and machine learning platforms at scale.

• A builder-operator mindset with robust CI/CD, observability, and incident response skills.

• A pragmatic practitioner who prioritizes reliability, adoption, governance, and impact over unnecessary complexity.

🏝️ Benefits

• Competitive salary.

• Company bonus potential.

• Medical, dental, and vision coverage.

• 401k with matching contributions.

• Generous paid time off.

• Complimentary gym membership to over 13,000 fitness locations across the US.

• Additional excellent benefits.