Senior Data Scientist - Vaga afirmativa para pessoas negras

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist - Vaga afirmativa para pessoas negras based in Brazil.

This is a high-impact data science role focused on building and deploying scalable machine learning solutions that directly influence business outcomes across complex digital ecosystems. You will work end-to-end on data science initiatives, from data exploration and feature engineering to model development and production deployment. The role sits within a collaborative, fast-paced environment where data, engineering, and product teams work closely to solve real-world problems. You will contribute to improving model performance, ensuring data quality, and driving experimentation at scale. Beyond model building, you will play a key role in translating business challenges into analytical solutions with measurable impact. This position also emphasizes mentorship, technical leadership, and knowledge sharing within a highly collaborative data team. It is an opportunity to grow in an environment that values scientific rigor, innovation, and continuous learning.

• Design, develop, and deploy end-to-end machine learning models, covering data acquisition, feature engineering, training, validation, and production deployment.
• Collaborate with cross-functional teams including data analysts, engineers, and product managers to translate business problems into data science solutions.
• Develop advanced features and work with large-scale datasets to improve model accuracy, robustness, and business impact.
• Ensure data quality and integrity by defining requirements for reliable datasets and monitoring data health over time.
• Monitor and troubleshoot deployed models, addressing issues such as drift, degradation, and performance bottlenecks.
• Provide technical leadership in statistical modeling, machine learning, and advanced analytics applied to complex business problems.
• Document and communicate methodologies, insights, and recommendations to both technical and non-technical audiences.
• Mentor and support junior data scientists and analysts through code reviews, technical guidance, and knowledge sharing.
• Contribute to a culture of experimentation, scientific rigor, and continuous improvement within the data team.

Requirements

• Degree in Computer Science, Statistics, Engineering, Economics, or another quantitative field.
• Strong proficiency in Python for data science (pandas, NumPy, scikit-learn, PySpark).
• Hands-on experience building and deploying machine learning models, including supervised and unsupervised learning techniques.
• Experience with large-scale data environments and cloud platforms such as GCP, AWS, Azure, or Databricks.
• Familiarity with data warehouses and databases such as BigQuery, Snowflake, PostgreSQL, or SQL Server.
• Experience with MLOps tools and workflows such as MLflow, Databricks, SageMaker, or Vertex AI.
• Solid understanding of Git-based version control workflows (GitHub, GitLab, Azure DevOps).
• Strong analytical thinking, problem-solving skills, and ability to work across multiple teams and stakeholders.
• Excellent communication skills and ability to present complex topics clearly to technical and non-technical audiences.
• Curiosity, collaboration mindset, and entrepreneurial spirit with a focus on continuous learning and innovation.
• Fluent English is required for working in an international environment.
• Nice to have: experience with deep learning frameworks such as PyTorch or TensorFlow/Keras.
• Nice to have: experience mentoring or coaching junior team members.

Benefits

• Meal and transportation assistance (VR/VT).
• Flexible work model with freedom to work from home or remote locations.
• Health, dental, and life insurance coverage.
• Gympass access for wellness, fitness, and health programs.
• Access to coworking spaces through flexible booking platforms.
• Semiannual performance reviews with growth and promotion opportunities.
• Regular in-person company gatherings with workshops, training sessions, and networking events.
• Collaborative and inclusive culture focused on empathy, learning, and innovation.
• Strong emphasis on professional development, experimentation, and continuous learning.