Senior Operations Research Scientist
Deliv (Menlo Park, CA)
Lead design of scientific approach that enables Deliv to extract patterns from operational behaviors and untangle different business processes to identify root causes for defects and key drivers for improvement; design optimization models; use linear programming; resolve transportation/assignment problems and network flow problems (shortest path; transportation decisions); perform statistical intervals for single sample (interval estimation, interval, prediction, tolerance interval); ues Simple Linear Regression/Correlation; use model multi-depot vehicle routing problem with time and capacity constraints; use vehicle routing problem literature to improve routing algorithms; use decomposition methods to solve large scale optimization problems; work with Metaheuristics (Ant Colony Optimization algorithms, stochastic local search); solve applied network flow problems; solve Quadratic/Integer/Mixed-Integer programming problems; develop optimization models for forecasting, scheduling, and routing; develop algorithms for optimal solutions; write scripts (Perl, Python, Ruby) to manipulate data or develop algorithms; develop machine learning algorithms to predict business; use machine learning libraries (Scikit-learn, TensorFlow); use Regression models, Stochastic modeling, Markov Chains and Bayesian Models; work with business partners across key organizational teams to understand core business challenges; design analytical models to identify operational patterns and improvement opportunities in quality and costs; collaborate with engineers and business intelligence to implement models for large analytical platform; lead research initiatives to offer operational recommendations for strategic decision making to the senior leadership teams; work with business analysts, business intelligence and system development engineers; and supervise others.
Master’s degree or foreign equivalent in Industrial Engineering or Operations Research plus coursework, internships, or experience to include linear programming, transportation/assignment problems, network flow (shortest path; transportation decisions); statistical intervals for single sample (interval estimation, interval, prediction, tolerance interval) and Simple Linear Regression/Correlation. Experience and skills may be gained through academic coursework and concurrently while pursuing academic studies.