Cientista de Dados SR

Accountabilities:

• • Develop, implement, and continuously improve allocation and optimization algorithms for distributing products across distribution centers and stores.
  • Maintain and enhance inventory simulators used for scenario analysis, replenishment policy testing, backtesting, and what-if simulations.
  • Transform data science prototypes and notebook-based models into scalable, maintainable, and production-ready solutions.
  • Build, maintain, and optimize large-scale data pipelines that support analytical and operational models.
  • Analyze business indicators such as service level, stockouts, inventory turnover, and fill rate, translating findings into actionable strategic recommendations.
  • Communicate analytical results, recommendations, and improvement opportunities clearly to business stakeholders and technical teams.
  • Partner with Data Engineering teams to build, operate, and evolve cloud-based pipelines, particularly within AWS environments.
  • Ensure the quality, reliability, scalability, and performance of analytical solutions running in production.
  • Contribute to the continuous evolution of replenishment, supply planning, and inventory management models and practices.
  • Help define and implement best practices across Data Science, analytical engineering, and scalable solution development.
  • Where applicable, provide technical leadership and mentorship to other Data Scientists and contribute to the development of the broader data community.

  Requirements:

  • Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related field.
  • Solid professional experience in Data Science, including the development and implementation of solutions addressing real-world business challenges.
  • Advanced Python skills, including strong experience with libraries such as NumPy and Pandas.
  • Strong SQL skills for querying, manipulating, and analyzing data.
  • Solid knowledge of statistics and probability applied to forecasting, inventory management, and service-level metrics.
  • Hands-on experience with PySpark for processing and transforming large volumes of data.
  • Proven experience building, maintaining, and optimizing large-scale data pipelines.
  • Experience with AWS services, particularly AWS Glue and Amazon S3.
  • Strong analytical and problem-solving capabilities, with the ability to turn complex data into actionable insights and strategic recommendations.
  • Excellent communication skills and the ability to collaborate effectively with both technical teams and business stakeholders.
  • Knowledge of Polars, Numba/JIT optimization, allocation algorithms, optimization, or operations research is a strong plus.
  • Experience with multi-echelon inventory simulation, replenishment strategies, Streamlit, Plotly, Terraform, Infrastructure as Code, CI/CD, or AWS Step Functions is desirable.
  • Experience providing technical leadership or mentoring Data Scientists is an additional advantage.

  Benefits:

  • Opportunity to work on complex, high-impact data science challenges with direct business impact.
  • Exposure to large-scale data, cloud technologies, optimization, simulation, and advanced analytical solutions.
  • Collaborative environment focused on innovation, continuous learning, and technical excellence.
  • Opportunities for technical leadership, mentoring, and professional development.
  • Interaction with multidisciplinary teams and stakeholders across different business areas.
  • Opportunity to contribute to scalable production solutions rather than working exclusively with experimental models.
  • A diverse and collaborative culture that encourages knowledge sharing and continuous growth.