Senior Data Scientist
FCamara Consulting & Training
full-time
Posted on:
Location Type: Remote
Location: Brasil
Visit company websiteExplore more
Job Level
About the role
- Act as a technical reference in MLOps, promoting best practices among data science and engineering teams.
- Collaborate cross-functionally with data scientists, engineers, architects and product squads to ensure the delivery of robust, scalable solutions.
- Take a proactive stance in problem-solving, contributing to the continuous improvement of processes, tools and ML architecture.
- Support incidents in staging and production environments, leading root-cause identification, fast remediation and improvement recommendations.
- Demonstrate autonomy and ownership in defining deployment strategies, model architecture and monitoring.
- Translate technical and business needs into practical, sustainable production solutions.
- Promote knowledge sharing, contributing to the team's technical growth and fostering a culture of operational excellence.
- Maintain a product mindset and a systemic view of the full lifecycle of ML models in production.
Requirements
- Proven experience developing and deploying machine learning models on cloud platforms such as GCP, Azure, or AWS.
- Strong command of CI/CD practices applied to the ML lifecycle, using tools such as Jenkins, Azure DevOps Pipelines, Cloud Build, among others.
- Hands-on experience using Airflow to automate batch pipelines.
- Experience orchestrating online model serving via APIs using Kubernetes (K8s) and managed services like Cloud Run.
- Ability to build end-to-end pipelines for retraining, deployment and monitoring using tools like Kubeflow, Vertex AI Pipelines, etc.
- Experience deploying containerized models at scale in cloud environments, including defining autoscaling parameters and resource allocations.
- Familiarity with canary, blue/green and shadow deployment strategies to minimize production risk.
- Advanced knowledge of model and infrastructure monitoring tools such as Prometheus, Grafana, Rancher and custom dashboards.
- Ability to design cloud ML architectures aligned with project requirements, considering cost, performance and scalability.
- Experience implementing streaming data pipelines for real-time ingestion, processing and consumption.
- Solid understanding of MLOps principles, including versioning, lineage, testing and model lifecycle management.
- Desirable experience building internal tools or frameworks (such as FenixAI) to standardize and empower teams' autonomy over ML infrastructure.
Benefits
- Diversity
- Respect
- Ethics
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningCI/CD practicesAirflowKubernetesKubeflowVertex AI Pipelinescontainerizationstreaming data pipelinesMLOps principlesmodel lifecycle management
Soft Skills
problem-solvingautonomyownershipcollaborationknowledge sharingproactive stancecommunicationoperational excellencetechnical growthproduct mindset