Model Development Role - BFSI

Responsibilities:

- Required to work individually or as part of a team on data science projects and work closely with business partners across the organization.

- Planning and analytics

- Process mapping, process implementation, documentation, and quality review

- Implement model governance, provisioning requirements, and frameworks, and mange risks

- He/she would perform various complex activities related to statistical/machine learning models. Provide analytical support for productionalizing, evaluating, implementing, monitoring and executing models across business verticals using technologies including but not limited to Python, Spark, and H2O etc.

- Develop dynamic dashboards; analyze key risk parameters to help understand changes in business and model performance

- Identify opportunities and deliver process improvements, standardization, rationalization and automations. Enhance and standardize performance analysis, reporting packages

Essential Qualifications:

- BS degree or higher in a quantitative field such as applied math, statistics, physics, accounting, finance, economics, econometrics, or business/social and behavioral sciences with a quantitative emphasis

- Bachelors or Master's degree in engineering field like computer science and engineering, Information technology, Electrical Engineering, Electronics and Telecommunication Engineering etc.

- 4+ years of relevant experience

- Experience in implementing, productionalizing, model monitoring of supervised, unsupervised and semi-supervised model techniques including but not limited to Random Forest, GBM, Ridge-Lasso-ElasticNet, XGboost etc. Time-series techniques like Arima (and the family), Arch, Garch etc.

- Experience implementing, productionalizing, model monitoring of machine learning algorithms such as support vector machines, decision trees, logistic regression, clustering, neural networks, graphical models etc.

- Excellent understanding of model metrics including AUC, ROC, CAP-curve, F-statistics etc. with clear understanding of how model performance is tuned

- Excellent hands-on on with Drift and Lift analysis and related model monitoring metrics

- Experience in implementing, model monitoring of Deep-learning, Artificial intelligence techniques like ANN, CNN, DNN, RNN etc. and how to strategize deep-learning layer and activations.

- Strong programing skills.

- Expertise in one or more analytic tools like: Python (with Anaconda), Scala, Java, Scripting, H2O

- Experience in one or more of Big Data skills - SQL, Aster, Teradata, Hadoop, SPARK, H20 and various big data distributions like Hortonworks and MapR

- Model Monitoring for NLP, Text mining, Image/Voice processing, digital analytics, deep learning, machine learning models

- Proven experience in identifying threshold values for the related model monitoring metrics and suggestions being passed on to data scientist and business when model re-training required.

- Demonstrate excellent organization skills throughout the development of analytical solutions (data analysis documentation, hypothesis documentation, code management, etc.)