Responsibilities:
1. Design, develop and deliver models for operational problems using
modeling tools.
Apply mathematical programming knowledges to design and provide decision
support tools and software.
2. Apply advanced data mining and optimization techniques to solve complex
business problems including pricing, inventory, logistics, supply chain
management, scheduling, etc. Utilizing code (Python, R, Java, C++ or another
object-oriented language) for modeling (optimization, simulation,
statistical) and developing proof-ofconcepts.
3. Perform quantitative, economic, and numerical analysis of the performance
of these systems under uncertainty using statistical and optimization tools
such as Python and R to find both exact and heuristic solution strategies
for optimization problems.
4. Closely collaborate with other departments, integrate optimization
algorithms with other deliverables into larger business solutions.
5. Explain complex models and analysis using simplified terms, report and
present to decision makers.
Qualifications:
1. Ph.D. degree in Operations Research, Applied Mathematics, Industrial
Engineering, Decision Science, Theoretical Computer Science, or other
related scientific disciplines.
2. 5+ years of industrial experience in applying operations research
techniques to solve real-world problem. Research expertise in at least one
of the following: optimization theories, planning, revenue management,
inventory control, logistics.
3. Experience implementing models through modelling languages. Professional
experience in both academic and industrial settings is a plus. Experience
applying optimization models for strategic and tactical business decisions
is a plus.
4. Demonstrated software development skills in a collaborative team
environment, experience with programming in Python, R, Java, C/C++, or a
similar language a plus. Exposure to scripting languages, relational
databases and Linux.
5. Experience with manipulating complex data sources and very large-scale
data,
Familiarity with technical tools for analysis - Python (with Pandas, etc.),
R, SQL.
Python skills and previous software engineering background a plus.
6. Good communication skills with both technical and business people.
Ability to speak at a level appropriate for the audience.
