This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Python Data Science | Mid/Senior based in Brazil.
In this role, you will develop advanced optimization solutions to solve complex operational challenges and support data-driven decision-making. You will apply Operations Research and mathematical optimization techniques, with a strong focus on Mixed-Integer Linear Programming (MILP), to model and optimize real-world scenarios. Working primarily with Python and open-source optimization libraries, you will build scalable, reliable, and maintainable solutions. You will also contribute to technical and economic feasibility studies, scenario simulations, and financial analyses. Collaborating in a technology-driven environment, you will help translate business needs into practical, high-impact models. This opportunity is ideal for a professional who combines strong analytical thinking with software engineering best practices and a passion for solving complex problems.
Accountabilities:
- Design, develop, and implement Operations Research solutions focused on mathematical optimization, particularly Mixed-Integer Linear Programming (MILP).
- Conduct technical and economic feasibility studies, evaluate alternative strategies, and model multiple operational scenarios to support informed business decisions.
- Perform reaction stoichiometry analyses and calculate Net Present Value (NPV) using real-value assumptions to assess project feasibility and economic outcomes.
- Build, optimize, and integrate mathematical optimization models in Python using open-source libraries such as Pyomo or OR-Tools.
- Develop and maintain RESTful APIs using FastAPI to make optimization capabilities accessible and integrate them into broader systems.
- Ensure code reliability, maintainability, and performance through unit testing, high test coverage, version control, and adherence to software development best practices.
- Collaborate with relevant stakeholders to translate complex operational requirements into robust, efficient, and scalable technical solutions.
Requirements:
- Strong experience in Operations Research, including mathematical modeling, process optimization, objective function formulation, constraint definition, and decision variable selection.
- Proven hands-on experience with Mixed-Integer Linear Programming (MILP) and mathematical optimization techniques.
- Experience conducting technical and economic feasibility studies, as well as simulation and operational scenario modeling.
- Advanced proficiency in Python, including the ability to develop clean, efficient, and maintainable code.
- Practical experience with open-source optimization libraries such as Pyomo or OR-Tools.
- Experience designing and developing REST APIs using FastAPI.
- Knowledge of relational databases, including SQL and PostgreSQL.
- Proficiency with version control tools, particularly Git and GitHub.
- Experience writing unit tests, maintaining high test coverage, and applying software quality assurance practices.
- Strong analytical and problem-solving skills, attention to detail, and the ability to translate complex business challenges into structured technical solutions.
- A collaborative mindset, clear communication skills, and a commitment to continuous learning and technical excellence.
Benefits:
- Meal and/or food allowance.
- Medical and dental insurance.
- Pet health insurance.
- Home office allowance for remote positions.
- Welcome kit during onboarding.
- Life insurance.
- Access to psychological and nutritional support through Zenklub.
- TotalPass wellness and fitness benefits.
- Continuous learning and certification programs with partners such as Adobe, Anthropic, AWS, Google, Microsoft, and Salesforce.
- Partnerships and discounts to support personal and professional development.
- Access to UOL content.
- Maternity welcome kit.
- Extended maternity and paternity leave.
- Employee referral program.
- Remote work model, offering flexibility and the opportunity to work from Brazil.