Analyst/Associate/Vice President - Quant Research - Investment Bank

We have an opportunity with an Investment Banking client. For the role of Quant Research- Analyst / Associate / VP.

Location : Mumbai

Credit EMM Modeller:

- Very strong Stochastic Modeling and Data Science background, including Statistics, Probability, Machine Learning and Deep Learning

- Excellent practical data-analysis skills on real datasets, including familiarity with methods for working with large data and tools for data analysis, e.g., Pandas, Numpy, Scikit-learn, TensorFlow, Keras, etc.

- Familiarity with Time-Series analysis using Deep Learning, as well as experience in Reinforcement Learning would be a plus

- Object Oriented Programming (OOP) and software design skills, preferably obtained using C++. Extensive Python experience would be a plus as would be experience with Reactive Programming

- Attention to detail: thorough and persistent in delivering production quality analytics

- Excellent communication skills; explains her/his thought process clearly and communicates model and strategy behaviors to a non-technical audience efficiently

- Ability to work in a high-pressure environment

- Pro-active attitude. Should have a natural interest to learn about our business, models, and infrastructure.

Credit Risk & P&L :

- Parallel/distributed computing experience a plus.

- Implementing calculations in our proprietary system, Athena

- Analyzing and improving the performance or our calculations.

- Improving the efficiency and accuracy of our processes through automation.

Credit EMM Developer :

- Very strong Object Oriented Programming (OOP) and software design skills are required, preferably obtained using C++. Extensive Python experience would be a plus

- Excellent communication skills are required in our interaction with trading, technology, and control functions

- Practical data-analysis skills on real datasets, including familiarity with methods for working with large data and tools for data analysis and visualization, e.g., Pandas, Numpy, Tableau, Qlikview, SQL, etc

- Strong interest and some experience in data engineering and big-data related database technologies and tools like (Spark, Kdb+, Onetick, Hadoop, AWS etc.)

- Excellent practical data-analysis skills on real datasets, including familiarity with methods for working with large amount of data and tools for data analysis, e.g., Pandas, Numpy

- Attention to detail and focus on quality of deliverables

- Excellent communication skills; explains her/his thought process clearly and communicates models and strategies behaviors to a non-technical audience efficiently

Wholesale Credit Model Frameworks :

- Ph.D or MS in a numerate subject (e.g. Applied Math, Physics, Computational Biology, Engineering, Math Finance, etc)

- Excellent quantitative programming skills in Python; C++ a plus

- Strong quantitative problem solving skills and experience applying them to model implementations

- Focus on functional and numerical testing through entire model development software cycle

- Must be self-motivated, pro-active, responsible and driven to deliver

- Experience with Subversion, automated build/test systems, code coverage, unit testing and release processes

- Experience implementing, integrating and deploying financial models end-to-end

- Knowledge of Wholesale Credit, CCAR, Allowance (IFRS 9/CECL), Basel II/III regulatory capital

Wholesale Credit Modeling :

- Hands-on programming in one or more of the following: R, Python/pandas.

Good to have :

- Experience in dealing with sizable datasets (parallel processing, code optimization)

- Solid theoretical and practical knowledge of probability methods and usual models: generalized linear models, time-series analysis, panel data

- Data mining background and experience: clustering, decision trees, logistic regressions

Good to have :

- Familiarity with classification problems applied to credit risk.

- Solid theoretical and practical knowledge of probability methods and usual models: generalized linear models, time-series analysis, panel data

- Data mining background and experience: clustering, decision trees, logistic regressions

Good to have :

- Familiarity with classification problems applied to credit risk.

- Exposure to CMBS and/or Commercial Real Estate.

- Exposure to mortgages.

Sejal Patil