Applied Mathematics

Nuvnext; Región Centro; Hybrid; MXN 300,000 - 540,000; Full time

Job summary

Nuvnext in Jalisco, Mexico, seeks a mathematically minded problem-solver to translate real-world operations into optimization models using Python.

You will formulate problems, compare classical methods with quantum-inspired ideas, design how improvements and their uncertainty are measured, and document what worked and why, then present measured improvements to the founders.

Qualifications

• Applied mathematics or actuarial science degree, finished or nearly finished.
• You code in Python.
• Rigor and honesty with results.
• You explain a model without hiding behind the formalism.

Responsibilities

• Formulate operational problems as optimization.
• Solve them with classical methods and compare them with quantum ones.
• Design how an improvement and its uncertainty are measured.
• Write down what worked, what didn't and why.

Skills

Python programming

Optimization

Applied mathematics

Education

Degree in applied mathematics or actuarial science

Tools

NumPy

SciPy

Job description

Routes, assignments, inventories and shifts are math problems before they are software problems. In Labs you formulate them, choose the method and measure whether it truly improves on what the client already does.

The role

• Formulate operational problems as optimization.
• Solve them with classical methods and compare them with quantum ones.
• Design how an improvement and its uncertainty are measured.
• Write down what worked, what didn't and why.

Your first 90 days

• Day 30

  You learn the Labs experiments and reproduce one.

• Day 60

  You formulate a real client problem.

• Day 90

  You present a measured improvement with its uncertainty.

Stack and tools

Python Optimization solvers NumPy / SciPy

What we look for

Must have

• An applied mathematics or actuarial science degree, finished or nearly finished.
• You code in Python.
• Rigor and honesty with results.
• You explain a model without hiding behind the formalism.

Nice to have

• Operations research.
• Bayesian statistics.

What working here is like

What you build goes into products that operate real organizations. You work directly with the founders, with no layers between your code and production, under the same rule we give the intelligence: a person signs what is critical.