Applied Scientist - Optimization

Optimized, Inc.; San Francisco (CA); On-site; USD 170,000 - 240,000; Full time

Job summary

A leading optimization firm in San Francisco is seeking an applied scientist to develop optimization and decision-making systems for supply chain challenges. The role involves designing algorithms, building quantitative models, and collaborating with ML engineers. Ideal candidates will have a PhD or MS in a quantitative field, strong optimization expertise, and proficiency in Python. This position offers a compensation range of $170,000 - $240,000 plus equity.

Qualifications

• Have a PhD or MS in operations research, applied math, CS, or a quantitative field.
• Strong foundations in optimization (linear/integer programming, combinatorial optimization, stochastic methods).
• Proficient in Python and scientific computing libraries (NumPy, SciPy, OR-Tools, Gurobi, or similar).

Responsibilities

• Design and implement optimization algorithms for supplier selection, cost modeling, and supply chain network design.
• Build quantitative models for risk assessment, lead time estimation, and demand forecasting.
• Develop simulation frameworks to evaluate agent decisions against real-world procurement outcomes.

Skills

Optimization techniques

Python

Scientific computing libraries

Communication of quantitative concepts

Education

PhD or MS in operations research, applied math, CS, or a quantitative field

Tools

NumPy

SciPy

OR-Tools

Gurobi

Job description

We're looking for an applied scientist to build the optimization and decision-making systems at the core of our agents. You'll work on cost modeling, supplier scoring, risk quantification, and multi-objective optimization, turning messy real-world supply chain problems into tractable computational ones.

Compensation range for this role is $170,000 - $240,000 + equity.

What you'll do:

• Design and implement optimization algorithms for supplier selection, cost modeling, and supply chain network design
• Build quantitative models for risk assessment, lead time estimation, and demand forecasting
• Develop simulation frameworks to evaluate agent decisions against real-world procurement outcomes
• Work with ML engineers to combine classical optimization techniques with LLM-based reasoning
• Validate models against customer data and continuously improve accuracy based on deployment feedback

You may be a good fit if you:

• Have a PhD or MS in operations research, applied math, CS, or a quantitative field
• Have strong foundations in optimization (linear/integer programming, combinatorial optimization, stochastic methods)
• Are proficient in Python and scientific computing libraries (NumPy, SciPy, OR-Tools, Gurobi, or similar)
• Have experience building models that ship to production, not just papers
• Can communicate complex quantitative concepts clearly to engineers and non-specialists
• Bonus: experience with supply chain optimization, logistics, or procurement modeling