Sr Engineer, Data Science & Machine Learning Operations - remote opportunity

Tivity Health is a leading provider of healthy life-changing solutions, and they are seeking a Senior Data Science & ML Ops Engineer. This hands-on role focuses on partnering with business leaders to design, test, and deploy machine learning solutions that drive measurable business outcomes.

Responsibilities:

• Partner directly with business stakeholders to identify opportunities where data and machine learning can improve decisions, efficiency, or outcomes
• Design experiments and hypotheses that can be validated quickly using available data and pragmatic modeling approaches
• Select and apply out-of-the-box machine learning algorithms (e.g., classification, regression, forecasting, clustering, optimization)
• Own models end-to-end—from data preparation and feature engineering through deployment, monitoring, and iteration based on real-world results
• Deploy ML models into production using AWS-native tooling and integrate them into operational workflows and downstream systems
• Implement ML training and inference pipelines on Amazon SageMaker, including pipelines, endpoints, model registry, and monitoring
• Ensure production readiness through versioning, validation, rollback strategies, and performance monitoring
• Monitor model performance (accuracy, drift, stability, business KPIs) and iterate based on real-world impact
• Participate directly in diagnosis and resolution of production issues affecting data pipelines or ML workloads
• Build and operate data ingestion and transformation pipelines across batch and event-driven workloads using AWS Glue, zero‑ETL integrations, Step Functions, EventBridge, and related services
• Collaborate closely with IT, Security, and Platform Engineering teams to align with enterprise security, compliance, and operational standards
• Use infrastructure as code (Terraform, CDK, or CloudFormation) to create repeatable, scalable environments
• Own and operate S3-based data lake infrastructure, including Iceberg table formats, AWS Glue Data Catalog, and AWS Lake Formation
• Implement and enforce data zone architecture (e.g., raw, curated, and consumption zones) to support governed data access and lifecycle management
• Define and apply data access controls using Lake Formation permissions and IAM-aligned policies
• Establish 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
• Build 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 ML platforms
• Apply cost-aware design when selecting data processing, training, and inference approaches
• Optimize Glue, SageMaker, and storage usage to deliver value efficiently at scale
• Continuously improve platform reliability, scalability, and cost efficiency as data and ML workloads grow

Requirements:

• 5+ years in a professional data science role and 5 years of experience with machine learning pipelines, preferably in an AWS environment
• Applied problem solver motivated by business outcomes and action
• Strong business partner able to translate questions into testable hypotheses and executable solutions
• Hands-on applied ML experience delivering models into production AWS environments
• Proven experience operating governed data lakes and ML platforms at scale
• Builder–operator mindset with strong CI/CD, observability, and incident response skills
• Pragmatic practitioner who values reliability, adoption, governance, and impact over unnecessary complexity