• Run end-to-end forecasting analyses on real business problems • Build and evaluate time series and machine learning forecasting models • Write clean, well-structured code and take part in code reviews • Take ownership of your tasks: estimate your work, communicate progress, and flag issues early to deliver on time • Support the full lifecycle of our forecasting products • Keep learning - deepening your technical foundations and forecasting expertise

• Definir a visão estratégica e o roadmap da área de Dados, alinhando iniciativas aos objetivos da empresa e do produto; • Liderar os squads de Migração e Qualidade de Dados, promovendo alinhamento, colaboração e desenvolvimento contínuo; • Planejar e acompanhar migrações de dados de novos clientes, garantindo qualidade, segurança e cumprimento dos SLAs estabelecidos; • Monitorar indicadores de desempenho relacionados à migração, qualidade e governança de dados; • Definir e evoluir processos, padrões e boas práticas para tratamento, armazenamento e utilização dos dados da plataforma; • Revisar e direcionar decisões técnicas relacionadas à arquitetura de dados, pipelines, integrações e qualidade da informação; • Garantir a conformidade com requisitos de segurança, privacidade e LGPD; • Atuar como ponto de escalonamento para migrações complexas e incidentes críticos relacionados a dados; • Apoiar as áreas de Produto, Engenharia, Customer Success e Suporte com análises, direcionamentos e priorizações relacionadas a dados; • Estruturar políticas de governança, rastreabilidade, documentação e classificação das informações da plataforma; • Identificar oportunidades de evolução da maturidade de dados da empresa, apoiando iniciativas de analytics, BI e inteligência de dados.

• Lead data ingestion, cleansing, transformation, and aggregation efforts for large scale and complex datasets. • Design and implement advanced feature engineering, statistical estimation, and hypothesis testing techniques. • Develop, validate, and refine machine learning and statistical models, including time series, repeated measures, and mixed effects models. • Ensure analytical rigor by addressing overfitting, false discovery, bias, and model generalizability. • Analyze healthcare and enterprise datasets to surface complex, high impact, actionable insights that support strategic decision making. • Drive iterative model development and support continuous integration and deployment of analytics solutions. • Optimize data science solutions for performance, scalability, and production readiness. • Leverage cloud based platforms to support elastic, high volume data science workloads. • Collaborate with business stakeholders, data engineers, architects, and analysts to align analytics outputs with business objectives. • Provide technical leadership and guidance to junior data scientists and analysts. • Contribute to the definition and evolution of data science standards, best practices, and reusable analytics assets.

Role Description The Data Science Operations Lead sits at the intersection of Data Science, Engineering, and IT Architecture: a senior individual-contributor role focused on the operational side of the model lifecycle, including deployment, monitoring, scaling, and maintenance. This role exists to keep a growing portfolio of models in production reliable, observable, and well-governed without pulling Data Scientists away from building new capabilities. - Model deployment partnership: Serve as Data Science's primary counterpart to the Architecture / Platform Engineering team on model deployment. Own the day-to-day collaboration, hand-offs, and coordination. - Production reliability and incident response: Act as first point of contact for production issues (outages, errors, degraded endpoints) across all deployed models and endpoints. This role carries an explicit on-call / off-hours availability expectation. - Resilient, error-aware systems: Bring rigor to error handling and fault tolerance. Design and enforce practices that prevent errors before they happen. - Monitoring and observability: Establish and maintain the monitoring and observability needed to manage a portfolio of production models. - Deployment expertise and team enablement: Develop a detailed understanding of the deployment system and help Data Scientists move from experiment to production quickly and safely. - Governance and quality: Own versioning, reproducibility, and operational governance for models in production. Qualifications - Hands-on experience operating ML or software systems in production: an MLOps, DevOps, SRE, platform, or data science background with demonstrated production ownership. - Strong working knowledge of CI/CD pipelines, deployment automation, and a major cloud platform (AWS, Azure, or GCP). - Demonstrated expertise in error handling, fault tolerance, and designing systems that fail gracefully. - Proficiency in Python (R a plus), and a working understanding of how ML models are packaged, served, monitored, and retrained. - Comfort serving as first point of contact for production issues, including an on-call / off-hours expectation. - A teaching disposition, with the ability to translate complex infrastructure into clear guidance for colleagues. Requirements - Experience standing up monitoring and observability for a portfolio of production models or services. - Familiarity with containerization (Docker) and orchestration (Kubernetes), infrastructure-as-code, and model-serving frameworks. - Familiarity with MLOps tooling such as MLflow, Airflow, or Kubeflow, or managed equivalents. - Experience working across an engineering/architecture boundary as a liaison or embedded operations partner. - Pragmatic use of AI tooling to accelerate operations and code-quality work. Benefits - For more information about our benefit offerings, please visit our careers page at https://www.echo.com/company/careers . - Compensation: $129,352.00-188,077.00 per year. - This role is eligible for a bonus that is based on a combination of personal and business performance.