Credit Risk Consulting* External (Al Rajhi / ADCB)

Credit Risk Consulting* External (Al Rajhi / ADCB)

1 Nos.
51003
Full Time
5.0 Year(s) To 10.0 Year(s)
20.00 LPA TO 30.00 LPA
Accounts / Finance / Tax / CS / Audit
Account/Finance/Tax consulting
Job Description:

Designation : Consultant/Sr.Consultant/Manager/Sr.Manager


Aptivaa Consulting is looking out for candidates with past experience in the risk management
space for its Risk Consulting group. The individual would be expected to work most of his/her
time working with client service teams across the globe on risk management (more specifically
modeling) issues, including credit risk as well as other risk types such as market and operational
risk. The candidate would be located in Mumbai, India. However, the travel to serve clients
onsite national and internationally is mandatory.


Key Responsibilities:
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 rating
model) 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 development
exercise.
3) Provide expert advice to consulting team of modeling related issues
4) Support development and maintenance of proprietary Risk management tools and other
knowledge development projects.
Employee Specifications:
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 experience
6) Risk Rating Development experience
7) 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.
We offer:
Interesting work on international projects
High growth work environment
Competitive salaries and benefit package
Ability to develop career in fast growing consulting firm

Company Profile

The rapidly changing regulatory landscape has increased the cost of compliance for banks & financial institutions worldwide. Most of these institutions are still clinging to rusty legacy technology because they are just "good enough" and still "work fine". The result is suboptimal automation leading to even more manual effort and cost.

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