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| Job Location | Bangalore |
| Education | Not Mentioned |
| Salary | Not Disclosed |
| Industry | IT - Software |
| Functional Area | Statistics / Analytics |
| EmploymentType | Full-time |
Pricing team is responsible for setting & auditing millions of prices every day and relies on analytics based systems including machine learning models to have accurate prices. Scale of this operation and the pricing risk involved requires us to evolve our analytics platform in the pricing quality team and we are looking for candidates who can assist in making key data-related decisions by extracting, analyzing, manipulating, tracking, internally managing and reporting data, as well as Key Performance Indicators (KPIs) by working with cross-functional teams. The right candidate will have demonstrated analytical thinking, problem solving ability and have experience in deploying small to medium scale machine learning models in a production environment. Strong business aptitude and ability to collaborate with multiple stakeholders is a must.About you: You, as the right candidate, are adept at executing every stage of the machine learning development life cycle in a business setting; from initial requirements gathering to through final model deployment, including adoption measurement and improvement. You will be working with large volumes of structured and unstructured data spread across multiple databases and can design and implement data pipelines to clean and merge these data for research and modeling. Beyond mathematical understanding, you have a deep intuition for machine learning algorithms that allows you to insist on defining the problem so that you can anticipate and suggesting appropriate algorithm applications. You re talented at communicating your results clearly to business owners in concise, non-technical language.What you will do Work with a team of operation managers, program managers and engineers to define business problems. Deliver quantitative research and develop predictive models in an Agile research and development environment. Use AWS services like SageMaker to build scalable ML models in the cloud. Support the analytical needs of the team inclusive of routine reporting, statistical inference, predictive modeling and simulation, 2+ years of professional work experience in Machine Learning, Data Science, Computer Science, Artificial Intelligence, Predictive Analytics or similar fields. Previous experience working in the areas of inferential statistics, machine learning, simulation and predictive modeling. Fluency in Python/R Has working knowledge of supervised learning algorithms; Decision trees, ensemble models (Random Forest, bagging and boosting), text analytics (parsing, pre-processing, topic modeling, text classification) and has the ability to build prototypes with quick turnaround using R (dplyr, mlr etc.) or python (pandas, numpy, scipy, scikit learn) and basic knowledge of regression and forecasting techniques. A Masters or PhD in statistics, economics, computer science, machine learning, operational research, or another highly quantitative field. Experience with the Operations, Marketing, Finance or Research is an added advantage Industry experience building/operating highly available, distributed systems for data analytics Experience with AWS ML and supporting services Strong communication skills in both technical and non-technical settings.
Keyskills :
life cycledecision treescomputer scienceunstructured dataproblem solvingstrong communication skillsprogram managersdata scienceagilemachine learningtext analyticsquick turnaroundproject management