Our Senior Data Science & ML Ops Engineer is a hands-on role focused on partnering with business leaders and technology teams to design, test, and deploy actionable machine learning solutions that drive measurable business outcomes. This role bridges data science, engineering, and operations—owning the full lifecycle from hypothesis and experimentation through production deployment and operationalization.This position is centered on applied machine learning, using proven, off-the-shelf algorithms and scalable AWS services to rapidly validate ideas, embed models into business workflows, and ensure they are reliably running in production.Business-Driven Experimentation & Model OwnershipPartner directly with business stakeholders to identify opportunities where data and machine learning can improve decisions, efficiency, or outcomesDesign experiments and hypotheses that can be validated quickly using available data and pragmatic modeling approachesSelect 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 resultsML Implementation, Production & OperationsDeploy ML models into production using AWS-native tooling and integrate them into operational workflows and downstream systemsImplement ML training and inference pipelines on Amazon SageMaker, including pipelines, endpoints, model registry, and monitoringEnsure production readiness through versioning, validation, rollback strategies, and performance monitoringMonitor model performance (accuracy, drift, stability, business KPIs) and iterate based on real-world impactParticipate directly in diagnosis and resolution of production issues affecting data pipelines or ML workloadsData Platform & Engineering CollaborationBuild and operate data ingestion and transformation pipelines across batch and event-driven workloads using AWS Glue, zero‑ETL integrations, Step Functions, EventBridge, and related servicesCollaborate closely with IT, Security, and Platform Engineering teams to align with enterprise security, compliance, and operational standardsUse infrastructure as code (Terraform, CDK, or CloudFormation) to create repeatable, scalable environmentsData Governance, Lake Architecture & Operational ExcellenceOwn and operate S3-based data lake infrastructure, including Iceberg table formats, AWS Glue Data Catalog, and AWS Lake FormationImplement and enforce data zone architecture (e.g., raw, curated, and consumption zones) to support governed data access and lifecycle managementDefine and apply data access controls using Lake Formation permissions and IAM-aligned policiesEstablish and maintain data governance practices, including schema management, schema evolution, and lineage trackingEnsure data assets are discoverable, auditable, and secure through cataloging, metadata management, and access controlsBuild end-to-end observability using CloudWatch, Datadog, pipeline SLAs, data quality checks, and model drift detectionEstablish operational runbooks and support procedures for governed data and ML platformsCost-Effective, Scalable ML & Data DeliveryApply cost-aware design when selecting data processing, training, and inference approachesOptimize Glue, SageMaker, and storage usage to deliver value efficiently at scaleContinuously improve platform reliability, scalability, and cost efficiency as data and ML workloads growQualifications5+ years in a professional data science role and 5 years of experience with machine learning pipelines, preferably in an AWS environmentApplied problem solver motivated by business outcomes and actionStrong business partner able to translate questions into testable hypotheses and executable solutionsHands-on applied ML experience delivering models into production AWS environmentsProven experience operating governed data lakes and ML platforms at scaleBuilder–operator mindset with strong CI/CD, observability, and incident response skillsPragmatic practitioner who values reliability, adoption, governance, and impact over unnecessary complexityThe salary range for this opportunity is $165,000 to $200,000. Compensation depends on several factors: qualifications, skills, competencies, and experience.Tivity Health offers a robust benefits package, which includes a competitive salary, company bonus potential, medical, dental, vision, 401k with match, generous paid time off, free gym membership to over 13,000 fitness locations in the US, and other great benefits.About Tivity Health® Inc. Tivity Health, Inc. is a leading provider of healthy life-changing solutions, including SilverSneakers®, ForeverFit®, and WholeHealth Living®. We help adults improve their health and support them on life's journey by providing access to in-person and virtual physical activity, social and mental enrichment programs, as well as a full suite of physical medicine and integrative health services. Our suite of services support health plans, employers, health systems and providers nationwide as they seek to reduce costs and improve health outcomes. Learn more at TivityHealth.
