Job Location: Hyderabad/Secunderabad, Bangalore/Bengaluru
In this role, you will:
- Need to work individually or as part of a team on data science projects and work closely with business partners across the organization.
- Perform various complex activities related to statistical/machine learning models. Provide analytical support for porductionalizing, 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
- Maintain documentation for implementation and monitoring processes across the team with focus on standardization of controls
Required 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), PySpark, 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.).
- Strong ability to develop partnerships and collaborate with other business and functional areas
- Experience determining root cause analysis
Desired Qualifications:
- Output deployment using appropriate technologies (HTML5, Shiny, Django, python)
- Working expertise in Tensorflow, Keras or Pytorch would be added advantage
- Ability to translate analytical data into useful business information
- Critical thinking and strong problem solving skills
- Ability to learn the business aspects quickly
- Knowledge of banking industry and products in at least one of the LOB such as credit cards, mortgage, deposits, loans or wealth management etc.
- Knowledge of functional area such as risk, marketing, operations or supply chain in banking industry.
- Ability to multi-task and prioritize between projects
- Ability to work independently and as part of a team
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