Senior Data Scientist

AgileEngine, LLC; Brasil; Presencial; BRL 180 000 - 240 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

100% remote

Flexible hours

Annual learning budget

Resumo da oferta

AgileEngine is seeking a Senior Data Scientist to develop, deploy, and maintain production ML models in a cloud-native enterprise setting. You will build data pipelines for ML workflows, perform EDA and feature engineering, and collaborate with engineers and business stakeholders to deliver scalable ML solutions using AWS SageMaker or equivalent platforms.

Required: 4+ years of production ML experience, strong Python and advanced SQL, and Git proficiency.

Qualificações

• 4+ years of experience building and maintaining production ML models.
• Experience with AWS SageMaker or comparable enterprise ML platform.
• Experience deploying and monitoring ML models in production.
• Advanced SQL skills.
• Git and version control experience.
• Ability to work independently in production environments.

Responsabilidades

• Develop, deploy, and maintain production ML models.
• Perform exploratory data analysis and feature engineering.
• Build and improve data pipelines that support ML workflows.
• Collaborate with engineering and business stakeholders to deliver scalable ML solutions.
• Contribute to AI/LLM-based capabilities where applicable.

Conhecimentos

Python

Advanced SQL

AWS SageMaker

Git

Production ML

Data pipelines

Ferramentas

AWS SageMaker

Descrição da oferta de emprego

We are looking for a Senior Data Scientist to develop, deploy, and maintain production machine learning models in a cloud-native enterprise environment. You will build and improve data pipelines supporting ML workflows, perform exploratory data analysis and feature engineering, and collaborate with engineering and business stakeholders to deliver scalable ML solutions using AWS SageMaker or equivalent enterprise ML platforms. The role requires 4+ years of production ML experience with strong Python and advanced SQL skills, and includes contribution to AI and LLM-based capabilities where applicable.

What you will do

• Develop, deploy, and maintain production machine learning models.
• Perform exploratory data analysis and feature engineering.
• Build and improve data pipelines that support ML workflows.
• Collaborate with engineering and business stakeholders to deliver scalable ML solutions.
• Contribute to AI/LLM-based capabilities where applicable.

Must haves

• 4+ years of experience building and maintaining production machine learning models.
• Experience with AWS SageMaker or another enterprise ML platform, such as Vertex AI or Azure ML, supporting production ML pipelines.
• Experience deploying and monitoring ML models in production, not only notebook-based development.
• Advanced SQL skills.
• Git and version control experience.
• Experience working independently in production environments.
• Experience building scalable ML solutions in enterprise environments.
• Familiarity with cloud-based ML platforms and production deployment best practices.
• Strong communication skills and the ability to work with cross-functional teams.

Nice to haves

• Experience with MLOps tools such as MLflow, Airflow, dbt, or similar.
• Experience with Snowflake.
• Marketing, growth, experimentation, or causal inference experience.
• Experience with LLMs or AI agents.
• Familiarity with Agile development practices.
• Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
• Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
• Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
• Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
• Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
• Well-being & support: access local well-being programs and people-focused support tailored to your location

We designed our hiring process to be fast, transparent, and convenient — so you always know what to expect and can move through the steps without unnecessary delays.

After applying, keep an eye on your inbox — including your spam folder. Occasionally, emails from our hiring platform, LaunchPod, may land there.

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