Sr Data Scientist Lead

Accountabilities:

• Lead the design and implementation of data science initiatives, defining methodologies, technical approaches, analytical frameworks, and best practices across projects.
• Develop, evaluate, and optimize statistical and machine learning models to solve complex business problems and generate actionable insights.
• Own the complete data science lifecycle, including data exploration, feature engineering, model development, validation, deployment, monitoring, and continuous improvement.
• Design and implement scalable AWS-based data science and machine learning solutions, applying appropriate cloud architecture and engineering practices.
• Provide technical leadership to Data Scientists and other technical professionals, guiding modeling decisions, architecture discussions, technical reviews, and engineering practices.
• Partner with business stakeholders, product teams, data engineers, and software engineers to translate business challenges into effective data-driven solutions.
• Monitor model performance and reliability in production, identifying opportunities to improve accuracy, scalability, efficiency, and business impact.
• Contribute to long-term data science strategy, analytical standards, methodologies, and practices that support organizational growth.
• Mentor team members, encourage knowledge sharing, and help establish a culture of technical excellence and continuous learning.
• Drive projects from initial concept through production delivery while balancing technical quality, business priorities, and practical implementation considerations.

Requirements:

• 6+ years of professional experience in Data Science, Machine Learning, Advanced Analytics, or a related field, including experience leading technical initiatives or teams.
• Strong hands-on expertise in statistical modeling, machine learning, predictive analytics, experimentation, and data-driven problem solving.
• Advanced proficiency in Python, with experience using data science and machine learning libraries such as Pandas, NumPy, and Scikit-learn or equivalent tools.
• Strong understanding of supervised and unsupervised learning, feature engineering, model evaluation, optimization, and production machine learning practices.
• Solid hands-on experience designing or implementing data science and machine learning solutions on AWS.
• Strong SQL skills and experience working with large datasets, data warehouses, and complex data environments.
• Demonstrated technical leadership skills, including the ability to lead technical discussions, make modeling and architecture decisions, mentor colleagues, and drive initiatives from concept to production.
• Strong analytical, critical-thinking, and problem-solving abilities, with a pragmatic approach to complex business and technical challenges.
• Excellent communication skills and advanced English proficiency, with the ability to explain complex technical concepts clearly to international teams, business stakeholders, and leadership.
• Comfortable working autonomously in a distributed, international environment and collaborating effectively across technical and business functions.
• Experience with AWS SageMaker, S3, Glue, Redshift, Lambda, EMR, or Athena is a strong advantage.
• Additional experience with production ML deployment and monitoring, MLOps, ML lifecycle automation, Spark, Docker, Kubernetes, Generative AI, LLMs, RAG, or NLP is desirable.
• AWS certifications related to Data, Machine Learning, or Solutions Architecture are a plus.
• Experience in financial services or other data-intensive industries is beneficial.

Benefits:

• 100% remote work from Brazil.
• Long-term engagement with international clients and global technical teams.
• High-impact technical leadership position with significant ownership and influence.
• Opportunity to shape data science and machine learning strategy and technical best practices.
• Exposure to cloud-based data and ML technologies, particularly the AWS ecosystem.
• Collaborative environment with experienced professionals across data, engineering, product, and business functions.
• Opportunities for mentoring, knowledge sharing, and continuous professional development.
• Strong autonomy and the opportunity to work on meaningful, business-focused technical challenges.
• Compensation and any additional employment benefits are subject to the partner company's applicable package and will be discussed during the hiring process.