Senior Data Scientist, Python, GCP, MLOps

Dasa

Cientista de Dados Sênior criando modelos de Machine Learning e GenAI para a maior rede integrada de saúde do Brasil. Liderança técnica de pipelines MLOps, deploy e monitoramento em produção.

Posted 8/18/2026full-timeSão Paulo • 🇧🇷 BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in leading machine learning projects, from problem definition to deployment, with a strong foundation in statistics and applied mathematics. Proficient in building automated data pipelines and translating complex technical concepts for cross-functional stakeholders.

Highest-signal resume keywords

Machine Learning Model DeploymentGoogle Cloud Platform (GCP)Python ProficiencyCI/CD Workflows for MLHealthcare Experience

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningDeep LearningPredictive ModelingNLPComputer VisionStatisticsLinear AlgebraSQLAutomated TestingCode Versioning

Soft Skills

CommunicationMentoringCollaborationProblem-SolvingTechnical Translation

Tools & Technologies

Vertex AIBigQueryCloud ComposerDockerKubernetesMLflowScikit-LearnPandasPyTorchTensorFlow

Industry Keywords

HealthcareDiagnosticsHospital EnvironmentsGenerative AILLMs

Tech Stack

Tools & technologies

AirflowBigQueryCloudDockerGoogle Cloud PlatformKubernetesPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Lead and execute the end-to-end machine learning project lifecycle, from defining the business problem and exploratory analysis to deployment, monitoring, and production support
Design and implement analytical solutions and model architectures for predictive modeling, NLP, computer vision, and Generative AI/LLMs
Build automated data, training, and inference pipelines, including CI/CD, versioning, automated testing, and drift monitoring
Serve as the technical bridge between business/product and engineering, translating healthcare ecosystem problems into actionable, measurable analytical hypotheses
Support the technical development of mid-level and junior data scientists, perform code reviews, and promote best practices
Build data-driven narratives to present technical metrics and business impact to executive and cross-functional stakeholders

Requirements

What you’ll need

Strong experience building and deploying machine learning/deep learning models into real production environments
Hands-on cloud experience, preferably with Google Cloud Platform (GCP)
Experience with Vertex AI, BigQuery, Cloud Composer/Airflow, Cloud Run, GKE/Dataproc
Strong foundation in statistics, linear algebra, probability, and applied mathematics for ML/AI algorithms
Knowledge of orchestration pipelines and CI/CD workflows for ML
Familiarity with containerization using Docker/Kubernetes
Code versioning with Git and model versioning with MLflow or Vertex Model Registry
Proficiency in Python and libraries such as Scikit-Learn, Pandas, PyTorch/TensorFlow and XGBoost
Advanced SQL querying skills
Code quality practices: SOLID principles, modularity, documentation, unit and integration testing
Prior experience with practical LLM/GenAI applications, including RAG, fine-tuning, LangChain/LlamaIndex and prompt engineering in production
Experience in healthcare, diagnostics, or hospital environments is a plus
Experience communicating with ML Engineers, Product Owners, and executive leadership is desirable
Experience working in multidisciplinary squads is desirable

Benefits

Comp & perks

Meal voucher/food allowance or on-site cafeteria
Health insurance
Life insurance
Dasa University
Development and career progression cycle
Technology Academies/PMAX
'Crescer' growth program within Dasa
Transportation allowance
Performance bonus (PPR)
Yoga classes
TotalPass (wellness benefit)
Primary care clinic
Discounts on exams and vaccines
UAU perks club
SESC benefits
Telepsychology