Consultant-Credit Risk

Description:

1. Solve analytically complex client problems in the Risk domain like developing Credit Risk Scoring Models for clients in the financial sector.2) Lead modeling workstream in client engagements (such as the development of a ratingmodel) which would include the below: Define data requirements for creating a model; Clean, aggregate, analyze and interpret data and statistical modeling output; Coach junior members of the team working alongside on the model developmentexercise.
2. Provide expert advice to consulting team of modeling related issues4) Support development and maintenance of proprietary Risk management tools and other knowledge development projects.

Requirements:

1. Post-graduate degree (preferably in Statistics or an MBA or Economics degree with a strong quantitative underpinning) from a respected institution (e.g., DSE, ISI, JNU, IIMs, MDI etc.) with an outstanding academic record.2) 1-9 years of professional work experience with a reputed bank, insurance, other financial firm or analytics firm with at least two years of experience in risk management.3) Specific exposure to either credit risk or market risk.4) Quantitative risk management experience is essential.5) Score card Development experience6) Risk Rating Development experience7) Candidates must demonstrate both a strong business sense and deep understanding of the quantitative foundations of risk management.8) Candidates must also possess the following hallmarks of excellent problem solving skills, team skills, entrepreneurial ability, and strong communication skills: Excellent analytical and problem solving skills, including the ability to disaggregate issues, identify root causes and recommend solutions. Good written and verbal communication skills. Fluent in both written and spoken English; prior experience in a multi-national environment desirable. Energetic, cooperative and pleasant personality. Able to work with and coach effectively a diverse group of team members Flexibility, patience, and an understanding of fluid, demanding, and unstructured environments where priorities evolve constantly and methodologies are regularly challenged. Ability to work under pressure and deliver on tight deadlines In addition, successful execution of this role will also need high levels of technical expertise. Candidates should have exposure (and some degree of expertise) to some of the following tools and techniques: Candidate should have sound knowledge on topics like Multivariate Statistics, Econometrics Analysis, Decision Tress(like CART, CHAID, etc), Optimization, Machine Learning ( artificial intelligence), stochastic processes etc. Tools like SAS, SPSS, Answer Tree, Crystal Ball, @Risk etc. Data mining tools like SAS Eminer, Knowledge Studio, Xeno, Model Builder,etc would be good.