Senior Data Scientist, Python, GCP, MLOps

Jobtailor

São Paulo

Presencial

BRL 180 000 - 360 000

Tempo integral

Há 5 dias

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Resumo da oferta

Jobtailor in São Paulo, Brazil, seeks an experienced ML leader to own the full project lifecycle from problem framing to deployment and monitoring. You will design architectures for predictive modeling, NLP, computer vision, and GenAI, while building robust data pipelines and ensuring production readiness.

The role involves collaborating with business, product, and engineering teams, mentoring data scientists, and communicating analytic impact to executives.

Qualificações

Experience deploying ML models to production in real environments.
Hands-on cloud experience with GCP and Vertex AI.
Strong knowledge of ML tooling and CI/CD pipelines for ML.
Background in healthcare analytics is a plus.

Responsabilidades

Lead end-to-end ML project lifecycle from problem framing to deployment.
Design architectures for predictive modeling, NLP, computer vision, and GenAI/LLMs.
Build automated data, training, and inference pipelines with CI/CD, versioning, and monitoring.
Bridge business/product and engineering, translating healthcare problems into actionable hypotheses.
Mentor mid-level and junior data scientists; perform code reviews.
Present data-driven narratives to executives and cross-functional stakeholders.

Conhecimentos

ML Deployment

GCP Expertise

Python Proficiency

SQL Proficiency

Docker & Kubernetes

MLflow/Vertex Registry

Scikit-Learn/Torch/TF

CI/CD for ML

GenAI/LLMs

Healthcare Domain

Ferramentas

Vertex AI

BigQuery

Cloud Composer

Cloud Run

GKE/Dataproc

Git

Pandas

Scikit-Learn

PyTorch

TensorFlow

Descrição da oferta de emprego

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

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

Core Competencies

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.

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