Intern, Data Scientist Oracle
Burlington, MA

Job Description

This job code is utilized for the majority of our temporary hires. The individual is performing hourly job duties as defined under the Fair Labor Standards Act.

Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.

Design, develop, troubleshoot and/or test/QA software. Define analytical approaches to solving challenging retail business problems in close collaboration with product management and solution architects. Develop and validate prototypes; test proposed solutions on real data. Write algorithmic specifications for forecasting, optimization, and data mining engines. Assist Development and QA with implementation and testing of new functionality. Assist with initial customer deployments of new functionality or new products. Present findings to internal and external audiences. Define and develop software for tasks associated with the developing, debugging or designing of software applications or operating systems. Provide technical leadership to other software developers. Specify, design and implement modest changes to existing software architecture to meet changing needs. Create patents and research new solutions for solving business problems

skills

1. Software Application design and development using Shell scripting, SQL, Java, C , Linux, Windows, and Oracle Database;

2. Algorithm development and implementation;

3. Publishing and presenting analytics research and results;

4. Analytic / Statistical languages such as R, SAS or Matlab;

5. Optimization engines such as Gurobi, Cplex or XpressMP;

6. Econometrics methods including regression techniques (such as OLS, MLE, univariate or multivariate analyses) and Statistics and Probability techniques (such as Bayesian methods, Kalman Filters);

7. Optimization algorithms and techniques including LP, MIP, QP, nonlinear programming and heuristics;

8. Simulation including Monte Carlo methods;

9. Data mining including classification and clustering algorithms;

10. Stochastic Demand Modeling and Forecasting;

11. Dynamic Pricing and Revenue Management Modeling

12. Machine learning