Senior Applied Scientist

Pride Global; Rio Claro; Presencial; BRL 180.000 - 240.000; Tempo integral

Resumo da oferta

A leading technology firm in Brazil is looking for a Senior Applied Scientist to lead the development and deployment of advanced Machine Learning solutions. This role involves influencing technical direction, collaborating with product and engineering teams, and driving innovation. The ideal candidate will have over 5 years of industry experience, a Master’s degree or PhD in a related field, and strong leadership capabilities. Key responsibilities include developing ML models, analyzing datasets, and mentoring junior staff.

Qualificações

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

Responsabilidades

• Lead end-to-end development and deployment of Machine Learning models.
• Analyze large-scale datasets to extract insights.
• Collaborate with cross-functional stakeholders to design production-ready ML solutions.

Conhecimentos

Applied Machine Learning

Leadership

Python

SQL

Deep Learning

Data Engineering

Cloud Platforms

Formação académica

Master’s degree or PhD in Computer Science

Ferramentas

PyTorch

TensorFlow

Keras

NumPy

pandas

Apache Beam

Spark

Descrição da oferta de emprego

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.

Qualifications

• 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.