Nomura Overview:
“Nomura is an Asia-headquartered financial services group with an integrated global network spanning over 30 countries. By ‘Connecting Markets East & West’, Nomura services the needs of individuals, institutions, corporates and governments through its three business divisions: Retail, Asset Management, and Wholesale (Global Markets and Investment Banking). Founded in 1925, the firm is built on a tradition of disciplined entrepreneurship, serving clients with creative solutions and considered thought leadership.
For further information about Nomura, visit www.nomura.com”.
Nomura Services India, (Powai) supports Nomura’s businesses around the world. Powai’ s world class capabilities in trading support, research, information technology, financial control, operations, risk management and legal support have played a key role in facilitating Nomura’s global operations and are an integral part of Nomura’s global expansion plans. The Powai operation is a critical part of the platform to support the growth of Nomura’s global business.
Divisional Overview:
The Risk Management Division encompasses the firm’s comprehensive risk framework responsible for determining and managing the overall risk appetite for the firm. The division is responsible for effectively managing the firm’s risk-return profile which ensures the efficient deployment of the firm’s capital. It is one of the firm’s core competencies and is independent of the trading areas and operational areas. The Risk Management Division in India comprises:
- Market Risk Management
- Credit Risk Management
- Risk Methodology
- Model Validation
Business Unit Overview:
Model Risk Management is a group within Risk Management headed by the Global Head of Model Risk responsible for:
(1) Executing and maintaining an effective Model Risk management framework.
(2) Producing a consolidated view of Model Risk for comparison with the Model Risk Appetite.
(3) Independently validating the integrity and comprehensiveness of the Models in the Firm.
Due to the extension of the scope of the Model Risk Management process, the Firm is seeking to recruit a member of the newly established Algorithmic Trading Model Validation Group. The successful candidate will have a strong quantitative background and will be responsible for the independent validation of Nomura’s Algorithmic Trading Models across a wide variety of asset classes / business lines.
Position Specifications:
Corporate Title
Vice President
Functional Title
Vice President
Experience
7 years plus
Key objectives critical to success:
The current role will specifically cover the following areas:
- Independent Validation of Algorithmic Trading Models, including
- Assessment of conceptual soundness of Algorithmic Trading Models, including the integrity and suitability of Model parameters
- Implementation Testing
- Model Risk Analysis – to identify, analyse and quantify Model Risk, which involves independent coding/development of the algorithmic trading strategy.
- Development and Execution of Model Performance Monitoring to ensure that Algorithmic Trading Models are performing as intended.
- Design and implementation of Model Risk Control processes for Algorithmic Trading Models
- Preparation of model review documentation
Mind Set:
Qualification, Experience & Skills: Essential
- A working experience in a quantitative environment either as a Model Developer or Model Validator
- A graduate/ postgraduate degree in a quantitative discipline
- Practical knowledge of optimization, statistics, and machine learning (e.g., classification, supervised and unsupervised learning)
- Hands-on experience with querying and analyzing big datasets, ideally high frequency tick data.
- Strong aptitude for code development and backtesting of trading strategies using python or similar.
- Excellent verbal & written communication skills in English and competent in delivering high-quality evidence-based reports.
- Self-motivated work attitude and ability to deal with various stakeholders.
Desirable
- Experience in scientific programming & data visualization in R or Python and its libraries (e.g.scikit-learn, tensorflow)
- Knowledge of q/kdb+
- PhD (or equivalent) in a quantitative discipline
