Senior Applied Scientist

Pride Global; Teletrabalho; BRL 432.000 - 649.000; Tempo integral

Resumo da oferta

A leading tech firm is seeking a Senior Applied Scientist for a remote position in Brazil. The role involves developing and deploying Machine Learning solutions, influencing technical strategies, and collaborating with cross-functional teams. Applicants should have over 5 years of experience in applied Machine Learning, strong technical expertise, and leadership capabilities. This opportunity is ideal for candidates passionate about building impactful solutions in the field of data science.

Qualificações

• 5+ years of industry experience in applied Machine Learning.
• 3+ years of experience building, deploying, and managing ML models in production.
• Strong understanding of ML best practices.

Responsabilidades

• Lead development and deployment of Machine Learning models.
• Analyze large-scale datasets for insights.
• Collaborate with cross-functional teams to design ML solutions.

Conhecimentos

Machine Learning

Python

Deep Learning

Data Engineering

Cloud Platforms

SQL

Formação académica

Master's degree or PhD in Computer Science or related field

Ferramentas

PyTorch

TensorFlow

Scikit-Learn

Apache Beam

Spark

Descrição da oferta de emprego

Senior Applied Scientist (Remote – Brazil)

Employment Type : Full-Time

Work Location : Remote (Brazil) – São Paulo

Duration : Open-Ended

Job Overview

We are seeking a highly skilled Senior Applied Scientist to lead the development and deployment of advanced Machine Learning solutions. In this role, you will influence the technical direction of the team, collaborate closely with Product and Engineering partners, and drive innovation across data-driven initiatives.

This is an excellent opportunity for someone who combines strong technical expertise with leadership capabilities and a passion for building high-impact solutions.

Responsibilities

• Lead end-to-end development and deployment of Machine Learning models to solve complex business problems.
• Write and maintain clean, efficient, scalable code following industry best practices.
• Analyze large-scale datasets to extract insights and support data-informed decision-making.
• Apply state-of-the-art ML methodologies, including deep learning and modern frameworks, to build robust models.
• Take ownership of key system components and influence the broader technical strategy.
• Collaborate with cross-functional stakeholders to design deployable, production-ready ML solutions.
• Mentor junior scientists and engineers, fostering team expertise and growth.
• Contribute to organizational technical leadership through patent filings and publications in top-tier venues.

Minimum Requirements

• 5+ years of industry experience in applied Machine Learning.
• Master’s degree or PhD in Computer Science, Machine Learning, Statistics, Operations Research, or a related field.
• 3+ years of experience building, deploying, and managing ML and deep learning models in production at scale.
• Deep understanding of ML best practices (A / B testing, pipelines, feature engineering, experiment design).
• Strong knowledge of algorithms and techniques such as gradient boosting, deep neural networks, transformers, and optimization methods.
• Experience in Computer Vision ; experience in Causal Inference is highly desirable.
• Expertise with Python scientific libraries (NumPy, pandas, Polars) and ML frameworks (PyTorch, TensorFlow, Keras, Scikit-Learn).
• Strong data engineering skills and comfort working with large-scale datasets.
• Experience with big data tools (Apache Beam, Kafka, Spark).
• Experience with cloud platforms : AWS, GCP, or Azure.
• Fluency in Python and SQL .

Preferred Qualifications

• PhD in CS, ML, AI, Statistics, OR, or related fields.
• Experience applying ML to real-world retail industry problems.
• Familiarity with modern MLOps tools and pipelines .
• Impact-driven mindset and commitment to delivering high-quality models.
• Demonstrated leadership capabilities with experience guiding or managing technical teams.