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| Job Location | Pune |
| Education | Not Mentioned |
| Salary | Not Disclosed |
| Industry | Banking / Financial Services |
| Functional Area | DBA / Datawarehousing |
| EmploymentType | Full-time |
* Duties and Responsibilities - Drive data backed decisioning for customer wrt products, processes, offer strategies, channel selection, timing etc. Deploy ML / Deep learning methodologies for feature engineering Create and fill existing variables through model variable inference methodologies Create methodologies to utilize sparse data to predict known use cases for marketing and risk Establish relationships between customer information, events etc. to trigger marketing, risk use cases Examples of use cases to create capabilities for: Create and manage complex inferred variables recommendation engine, prospect propensity, channel propensity model, share of wallet, size of wallet, propensity to buy (when, where, how), propensity to repay etc. Create solutions based on Geo data - negative area marking, household score Create solutions for managing marketing properties on wallet, creating solutions to match marketing content to customers through various tags Create metrics to evaluate performance of the solutions created, enhance capabilities to better performance metrics Own execution and availability of solutions to teams for retrospective analysis, transaction recommendation / approval management , * 3+ years of experience building sparse data models: Neural Network / Deep Learning / Bayesian Networks / Monte Carlo Markov Chain / SVM / GBM / Lookalike models / Feature engineering Passionate about extracting value out of data by creating new variables, understanding base variable relationships 3+ years of experience in managing other team members in a formal or informal capacity 1+ years of experience doing quantitative analysis Experience initiating and driving projects to completion with minimal guidance Experience communicating the results of analysis Preferred Qualifications Bachelor Degree in Computer Science, Math, Physics, Engineering, or related quantitative field Understanding of statistical analysis, experience with packages, such as R, MATLAB, SPSS, SAS, Stata, etc. Experience with large data sets and distributed computing (Hive/Hadoop) ~1-3 years of industrial experience in implementing | managing very large data platforms Like Data Lake & Enterprise Data Warehouse Strong Hands-on experience in the Big data stack (Hadoop, Spark), R|Py, Hive is mandatory Experienced in creating large data pipelines (via tools and code level- Python R | Spark)
Keyskills :
banking bigdata enterprisedata computerscience bayesiannetworks performancemetrics statisticalanalysis quantitativeanalysis ml sas svm wrt secasesdeeplearning