Data Scientist

Role: Data Scientist Location: Remote Mexico Duration: Contract Skills to be evaluated: AWS SageMaker, Python, MLOPs, AWS, SQL, Spark, Docker, Kubernetes, GitHub Actions, & Deep Learning. Responsibilities 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. Required Qualifications & Skills 7+ years of experience in applied data science or machine learning engineering, including ownership of production model development and deployment. 5+ years hands‑on experience with AWS cloud services, with deep expertise in AWS SageMaker (training jobs, pipelines, feature store, model registry, multi‑model endpoints, serverless inference). Demonstrated experience architecting large‑scale, production‑grade ML pipelines using tools such as Airflow, Kubeflow, AWS Step Functions, or SageMaker Pipelines. Strong proficiency in Python, deep learning frameworks (TensorFlow, PyTorch), and advanced ML architectures (representation learning, forecasting, generative models, anomaly detection). Advanced knowledge of MLOps practices including CI/CD (GitHub Actions, CodePipeline), containerization (Docker), orchestration (Kubernetes/EKS), and monitoring (Prometheus, CloudWatch, Datadog). Experience with modern data ecosystems, including data lakes/lakehouses, Spark, Delta/Iceberg, and real‑time streaming pipelines. Strong understanding of model governance, Responsible AI practices, and production SLAs. Excellent communication and stakeholder‑facing skills, with the ability to simplify complex technical topics. Experience with collaboration tools such as Jira, Confluence, GitHub/GitLab. Preferred Qualifications & Skills Experience designing ML architectures for high‑availability, multi‑tenant, or regulated environments (financial services, healthcare, manufacturing, etc.). Background in predictive maintenance, anomaly detection, customer intelligence, personalization, or NLP, depending on domain. Expertise in event‑driven ML, near‑real‑time inference, and streaming architectures (Kafka, Kinesis). Familiarity with software engineering best practices, including SDLC, testing patterns, code quality automation, and system design principles. #J-18808-Ljbffr