• Design, develop, train, and deploy machine learning and AI models that process and analyze field equipment sensor data (time-series IoT, embedded device telemetry) alongside structured and unstructured datasets. • Build and refine predictive, prescriptive, and anomaly detection models using techniques such as regression, time-series forecasting, classification, clustering, and deep learning to support real-time or near-real-time decision-making. • Perform exploratory data analysis (EDA), data preprocessing, feature engineering/signal processing, and feature extraction on high-volume, noisy sensor data and multimodal datasets to surface patterns, correlations, and actionable insights. • Contribute to end-to-end AI workflows, including automated data ingestion, model training pipelines, inference at the edge or in the cloud, and continuous monitoring for model drift and performance degradation. • Apply statistical modeling, hypothesis testing, and experimentation methods (A/B testing, causal inference where applicable) to validate model performance and ensure robustness in dynamic operational environments. • Support the development and maintenance of reproducible, scalable ML pipelines using MLOps best practices, including model versioning, retraining, deployment (including edge/embedded constraints), and lifecycle management. • Collaborate with engineering, product, and domain experts to translate business problems (e.g., predictive maintenance, fault detection, process optimization) into well-defined data science solutions. • Perform data cleansing, validation, and collation activities to ensure models are accurate, reliable, and aligned with real-world operating conditions. • Solve complex technical challenges related to analytical toolsets that support engineering and operational decision-making. • Communicate technical findings, model performance metrics, and business value to internal stakeholders through clear visualizations, written reports, and presentations. • Explore and evaluate emerging techniques (e.g., generative AI for synthetic sensor data, edge AI optimization, multimodal data fusion) and recommend incorporation into production workflows where appropriate. • Assist in formulating and managing data-driven project requirements aligned with business needs and strategic company goals. • Provide subject matter input on analytical tools and methods to cross-functional product development teams. • Work with software and business development teams to support revenue opportunities tied to data science initiatives and product/service enhancements. • Support internal resources involved in research, product development, and ongoing production of data analytics deliverables.

• Provide leadership and strategic direction in solving business problems using advanced mathematical and statistical methods. • Build advanced time-series models to forecast trends in medical spend using hierarchical Bayesian models and temporal fusion transformers. • Ensure scientific rigor and reproducibility of model evaluation. • Optimize models for accuracy and interpretability. • Communicate findings to stakeholders from technical ICs to top leadership.

• Responsible for all aspects of clinical trial data management from study start up through database lock and study close. • Ensures optimized data collection, flow and access across EDC and non-EDC data sources. • Ensures accuracy, consistency, completeness and CDISC compliance of all clinical databases. • Operate as a key member of the Clinical Operations team.

• Apoiar o PM a criar e evoluir domínios que compõem um produto, na jornada do usuário. • foco em integração entre sistemas, conhecimento API, Webhook, SFTP e conceitos técnicos de integrações • Ajudar na evolução do seu domínio, junto ao PM, colaborando para a evolução do produto, a partir de oportunidades e alavancas do negócio. • Ajudar na organização e manutenção do backlog do produto, garantindo padronização das informações, revisão dos critérios de aceite; • Apoiar na comunicação e atualização das informações relacionadas ao domínio; • Conduzir reuniões de apresentações de produto com os clientes; • Conduzir ritos menores de produtos com o time técnico interno; • Conduzir reuniões com outras áreas internas sobre o produto.