Analyst, Revenue Management Operations Research (0024-0012)

Responsibility for driving improvements in corporate systems by analyzing forecast and revenue data and implementing creative solutions to improve profitability; Utilize statistical analysis, simulations, predictive modeling, or other analytical methods to analyze data and develop practical solutions to business problems; Collect, pull, and push data to Oracle, Teradata, and HDFS storage using SQL, Spark, and data analysis technologies; Identify, analyze, and solve problems, and evaluate methods and results; Utilize statistical techniques including clustering, regression, and time series analysis to improve forecasting systems; Perform data analysis, model building, and output testing using SAS, R, and Python; Utilize optimization techniques including linear programming, dynamic programming, and gradient descent to optimize revenue in the corporate network forecast; Present analysis and results to assist management with decision making; Automate manual processes to streamline the workflow of operations research groups; Interact with internal and external groups to ensure corporate systems meet the needs of all interested grow.

Work Schedule: 40 hours per week/8 a.m.-5 p.m./M-F.
Job Location: Fort Worth, TX

Education and Experience Requirements

Master’s degree in Operations Research, Statistics, Engineering, or related field, plus 1 year of experience as Analyst, Intern, Teaching Assistant, or any occupation in which the required experience was gained, plus demonstrated experience in: Linear Programming (LP); Integer and Mixed-Integer Programming (MIP); Nonlinear Optimization; Stochastic Modeling; Optimization Techniques; Probability Theory; Statistical Inference (confidence intervals, and hypothesis testing); Regression Analysis (linear, logistic, and multivariate); Time Series Analysis (ARIMA and exponential smoothing); Experimental Design and A/B Testing; Bayesian Statistics (basic modeling and inference); Python for Analytics (NumPy, Pandas, and SciPy); R for Statistical Computing; SQL for Data Extraction and Transformation; Automation; Data Analysis; Microsoft Excel, PowerPoint, and Word; Data Cleaning and Preprocessing Techniques; Data Visualization (Matplotlib, Seaborn, and Plotly); Optimization under Uncertainty; Numerical Methods (root finding, interpolation, and numerical integration); Linear Algebra. Experience may be gained during or through the course of graduate-level education.
Please copy and paste your resume in the email body (do not send attachments, we cannot open them) and email it to candidates at placementservicesusa.com with reference #0024-0012 in the subject line.

Thank you.