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.