Operations Research Scientist

Job Description:

As an Operations Research Scientist at Tredence, you will:

1. Perform Data Analysis and Interpretation:

o Analyze large datasets to extract meaningful insights and support decision-making processes. Use Python to process, visualize, and interpret data in a clear and actionable manner.

2. Develop and Implement Models:

o Create mathematical models to address complex business problems and improve operational efficiency. Ability to use solvers like CPLEX, Gurobi, FICCO Xpress. Free solvers like Pulp, Pyomo etc.

o Apply optimization techniques and heuristic methods to devise effective solutions.

o Design and implement algorithms to solve optimization problems and enhance existing systems with advanced techniques like column generation, decomposition techniques

o Test and validate algorithms to ensure accuracy and efficiency.

3. Collaboration and Communication:

o Work closely with cross-functional teams including data scientists, engineers, and business stakeholders to understand requirements and deliver solutions.

o Present findings and recommendations to both technical and non-technical audiences.

Qualifications:

· Education: Bachelor’s or master’s degree in operations research, Applied Mathematics, Computer Science, Engineering, or a related field.

· Experience: 1-2 years of professional experience in operations research, data science, or a related field.

· Technical Skills:

o Proficiency in Python, including libraries such as NumPy, pandas, SciPy, and scikit-learn, Pulp, Pyspark

o Strong background in mathematical modeling and optimization techniques.

o Experience with heuristic methods and algorithm development.

· Analytical Skills:

o Ability to analyze complex datasets and derive actionable insights.

o Strong problem-solving skills with a detail-oriented approach.

· Soft Skills:

o Excellent communication skills, both written and verbal.

o Strong team player with the ability to work independently and manage multiple tasks.

Preferred Qualifications:

· Experience with additional programming languages or tools (AMPL, LINGO, AIMMS).

· Familiarity with machine learning techniques and their applications in operations research.