Sr Data Scientist

hace 1 semana

Ciudad de México, Ciudad de México W3Global A tiempo completo

Role: Sr Data Scientist
Location: Mexico City, Mexico- Remote
Skill Set: Deep Learning: AWS Sagemaker, Python, MLOPs, AWS, SQL, Spark, Docker, Kuberenetes, Github Actions, Deep Learning
Experience: 7 to 10 Years
Job Description
Architect and own end-to-end machine learning systems
-from data ingestion and feature engineering to scalable training, optimization, deployment, and monitoring on AWS SageMaker.

Lead technical design for ML platforms and pipelines
, selecting the right AWS, open-source, and MLOps tooling to meet performance, cost, and governance requirements.

Develop advanced models using deep learning and statistical techniques,
and optimize them for distributed training, accelerated compute, and real-time or batch inference.

Build reusable ML components, templates, and CI/CD workflows
that improve reproducibility, compliance, and engineering velocity across the organization.

Operationalize production ML at scale
, including multi-model endpoints, model registries, feature stores, experiment tracking, drift detection, and automated retraining.

Troubleshoot complex model, data, and infrastructure issues
, delivering root-cause analysis and long-term fixes for reliability, latency, performance, and cost efficiency.

Partner with product, data engineering, cloud engineering, and IT/security teams
to embed ML solutions into mission-critical business systems with enterprise-grade resilience.

Establish and enforce ML governance best practices
including model documentation, lineage, versioning, auditability, and alignment with internal and regulatory standards.

Provide technical leadership and mentorship
to mid-level and junior ML/DS engineers, reviewing code, guiding architecture choices, and uplifting overall team maturity.

Evaluate emerging tools, frameworks, and AWS services
, advising leadership on how to modernize the ML stack and accelerate high-value use cases.

Represent ML engineering best practices internally
, influencing roadmap decisions, contributing to technical design reviews, and shaping long-term data/AI strategy.

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