Sr Data Scientist Lead

Job description

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. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr Data Scientist Lead based in Brazil. This is a high-impact technical leadership role for an experienced Data Scientist ready to combine hands-on expertise with strategic influence. You will lead the design, development, and delivery of data science and machine learning solutions that address complex business challenges. The role covers the full data science lifecycle, from problem definition and experimentation through modeling, deployment, monitoring, and optimization. You will work extensively with cloud-based data and machine learning environments, with a particular focus on AWS. Beyond individual technical contributions, you will guide technical decisions, mentor data professionals, and establish effective analytical and engineering practices. Working with international stakeholders, product teams, data engineers, and software engineers, you will help turn data into measurable business value.