Data Science and Risk Analytics Leader

1 day ago

Bengaluru, Karnataka, India beBeeRisk Full time US$ 1,50,000 - US$ 2,00,000

As a Quantitative Modeling Lead in Data Science and Analytics teams, you will play a pivotal role by developing the next generation of risk analytics solutions. The mission is to build models and infrastructure used for risk management of Market Risk such as Value at Risk (VAR)/Stress/Fundamental Review of the Trading Book (FRTB). The team will support the activities globally and work closely with Front Office and Market Risk functions to develop tools and utilities for model development and risk management purposes.

Job Responsibilities

  • Work on the implementation of the next generation of risk analytics solutions;
  • Assess model performance, perform back testing analysis and P&L attribution;
  • Improve performance and scalability of analytics algorithms;
  • Develop and enhance mathematical models for VaR/Stress/FRTB;
  • Assess the appropriateness of quantitative models and their limitations, identifying and monitoring the associated model risk;
  • Design efficient numerical algorithms and implementing high-performance computing solutions;
  • Design and develop software frameworks for analytics and their delivery to systems and applications.

Requirements

  • Advanced degree (PhD, MSc, B.Tech or equivalent) in Engineering, Mathematics, Physics, Computer Science, etc.;
  • 3+ years of relevant experience in Python and/or C++ along with proficiency in data structures, standard algorithms, and object-oriented design;
  • Basic understanding of product knowledge across a range of asset classes - Credit, Rates, Equities, Commodities, FX & SPG;
  • Interest in applying agile development practices;
  • Quantitative and problem-solving skills as well as research skills;
  • Understanding of basic mathematics such as statistics, probability theory;
  • Good interpersonal and communication skills, ability to work in a group;
  • Easily adaptable;

Preferred Qualifications

  • Experience applying statistical and/or machine learning techniques in the financial industry;
  • Knowledge of options pricing theory, trading algorithms, or financial regulations;
  • Experience using multi-threading, GPU, MPI, grid, or other HPC technologies;
  • Excellent knowledge on data analysis tools in python like Pandas, Numpy, Scipy etc;
  • Knowledge of advanced mathematics such as stochastic calculus;
  • Knowledge of front-end technologies like HTML, React, and integration with large data sets.

Role: Head - Data Science

Industry Type: Financial Services

Department: Data Science & Analytics

Employment Type: Full Time, Permanent

Role Category: Data Science & Machine Learning

Education

UG: B.Tech/B.E. in Any Specialization

PG: MS/M.Sc(Science) in Any Specialization

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