Location: Houston, TX 77077 - Onsite 4 days per week.
Overview of Role:
As an Optimization Data Scientist, you will be responsible for driving the development and implementation of models and algorithms to solve complex business problems. This role involves translating innovative ideas into practical solutions using sophisticated analytical techniques such as machine learning, optimization, and cluster analysis. Our ideal candidate would be skilled in using optimization methods like linear programming, integer programming, and dynamic programming, and have a background in creating and utilizing simulation models.
Company Description:
Our client manages a large portfolio of companies and has a large focus on upholding integrity and being held accountable to the highest ethical standards. They are committed to fostering a culture of open communication, teamwork, and personal development.
Role Description:
Develop and implement mathematical models and algorithms to solve complex business problems.
Apply optimization techniques like linear, integer, and dynamic programming.
Create and utilize simulation models to improve operations.
Collaborate with business units to develop quantitative solutions that integrate with their workflows.
Analyze large datasets to identify trends and drive continuous improvement and innovation.
Present model findings and strategic recommendations to senior management for informed decision-making.
Skills and Experience:
Bachelor's degree in a related field with 4 years of experience, or equivalent (Master's with 2 years, Ph.D. with 1+ year, or 8 years of experience).
Expertise in optimization algorithms and large-scale models using techniques such as linear programming, mixed integer programming, and heuristics.
Proficiency in Python, C++, and build automation, with a strong background in optimization libraries like CPLEX or Gurobi.
Knowledge of operations research and mathematical modeling for supply chain optimization; automotive supply chain experience is a plus.
Experience with machine learning algorithms and analytics platforms (Python, R), with strong data manipulation and statistical analysis skills.
Strong collaboration and communication skills, with the ability to convey complex information to non-technical stakeholders and drive a data-driven culture.