Thursday, 17 October 2013

Portfolio Management Accelerated Track for Senior Associates

Hedge Fund seeking top PhDs and MS graduates with strong engineering/math aptitude for unique entry-level systematic portfolio management opportunity.  Learn statistical arbitrage and algorithmic trading techniques from senior portfolio managers.
Job Responsibilities (include, but not limited to the following):
We are seeking individuals with outstanding academic background to join us as Senior Associate, Accelerated Portfolio Management Track. The successful candidates will work under the guidance of our highly successful portfolio managers and Chief Investment Officer. After gaining enough experience, Senior Associates will be promoted to Portfolio Manager, and begin to manage their own investment portfolio to generate profits. This is a unique entry level opportunity for high caliber individuals looking for an accelerated track to a highly rewarding career in systematic trading. Successful applicants will receive compensation commensurate with their experience and academic achievement.
Job Qualifications:
-       Ph.D. or M.S. degree from a top tier institution in a highly analytical field, such as Mathematics, Operations Research, Optimization, Electrical Engineering, Financial Engineering, Computer Science and Physics
-       Interest/background in finance, demonstrated by coursework in economics or finance
-       Solid programming skill (C++/Python/Perl) is a strong plus
-       Ability to work independently and innovate
-       Understanding of optimization theory and algorithms (including dynamic programming, large-scale linear and non-linear programming, interior point methods, genetic algorithms, simulated annealing and robust optimization) is a plus
-       Strong work ethic and drive to succeed
Job description:
PositionPre-trade quantitative researcher
Position Description: Working in a strong team which builds and implements statistical models for equity, futures, and FX markets. The position assumes conducting research, development and maintenance of mathematical/statistical models that can be used in the trading process and for post-trade analysis. These models are utilized as trading tools or to assess the performance of institutional clients through peer group comparison, performance analytics and cost attribution. The position will include maintenance and enhancements of existing transaction cost models (ACE and SCE) as well as statistical analysis of empirical execution data to help improve analytics and trading strategies. Only those with solid technical and modeling skills are encouraged to apply.
Responsibilities of the pre-trade researcher include:
  • working closely with the entire financial engineering group to conduct formal quantitative analyses from the research question formulation to presentation of outcomes, typically including written summaries and/or business recommendations;
  • formal modeling of risk, return, and trading cost profiles for equities and other asset classes;
  • maintenance and support of existing models and products;
  • project-specific guidance to other team members in performing analyses and maintenance;
·         identifying issues and areas in need of statistical analysis.
Educational and Professional Requirements: 
  • Ph.D. or M.S. in a quantitative field (e.g. applied mathematics, physics, computer science, etc.) Recent graduates are encouraged to apply.
  • A strong desire to develop and integrate quantitative skills within the required scope of designing and implementing analytic solutions.
  • Ability to translate research into usable, value-added tools and information.
  • Strong ability to effectively communicate quantitative topics and concepts.
  • Strong written and verbal communication skills.
  • Working knowledge of C/C++ a must.
  • Knowledge of Perl, R/S-Plus or other scripting languages preferred
  • Familiarity with Unix environment and general Unix tools preferred.
  • Familiarity with SQL or other databases desired.
  • Strong knowledge of applied statistics and probability theory.
·         Relevant research or professional experience in financial and market microstructure modeling is a plus but not needed
·         Highly self-disciplined, detail- and results-oriented.
Sr. Quantitative Algorithmic Developer for Systematic Trading Strategies
Main Function
The Quantitative Strategies Trading team is a proprietary trading desk that builds black box algorithmic systematic trading models. These automated algorithmic trading strategies are built using C++ running on Unix/Linux and are deployed within the firm’s data centers as well as multiple co-location sites. Excellent, cutting-edge technology is the key to the desk’s profitability. Low-latency speed/performance is a competitive advantage for the strategies – with financial market data event to trading order submission being measured in microseconds. These systematic strategies send millions of orders per day, so scalability and stability is vitally important.
As the group expands into more trading financial products and markets we have a need to hire highly skilled C++ developers who can develop/code high speed trading products under tight deadlines. It would also be an advantage for the candidate to be experienced with and able to understand trading models in order that they can be implemented in our systematic strategy framework.
It is likely that in his or her role, the candidate will be developing new equity systematic trading infrastructure and many other areas associated with high speed/high frequency, low latency trading. Candidates will be responsible for the maintenance of the high performance trading infrastructure in the form of our low-latency systematic trading and market data drivers. In addition, the candidate will be comfortable working in a rapidly changing environment with short deadlines and a need for high quality reliable deliverables.
Personal Requirements
Basic Qualifications:
·         Bachelors Degree in Mathematics, Engineering or Computer Science
·         7+ years C/C++ development experience on Unix/Linux
·         7+ years knowledge of equity trading and financial market data interfaces
·         7+ years understanding of code optimization and high performing trading, low latency applications
Preferred Qualifications:  
·         PhD/Masters -Advanced degree in Mathematics, Physics, Engineering or Computer Science
·         Team Player capable of developing industrial, high performance strength software components
·         Experience of developing financial market interfaces or market data handlers
·         Good knowledge and understanding of systematic algorithmic trading
·         Ability to convert quantitative algorithm trading design into code
·         Unix/Linux Shell scripts
·         Python scripting
·         US equities and futures market microstructure
·         Experience of developing against US equities and futures APIs
·         FIX 4.x/5.x protocol
·         _________________________________________________________________________________________________________________________________________
Quantitative Data Analyst for Model Validation Testing (Algorithmic Trading)
This financial company is a private institutional investment management complex consisting of an international team of researchers, traders and technologists who constantly work toward even greater quantification and automation in the development of its trading processes. Continuously evolving for ever-greater efficiency enables us to trade today the way others will tomorrow.
We are seeking a Quantitative Data analyst for Model Testing to implement and maintains various quality tests and production systems on our quantitative trading models. These trading models are building blocks of our trading strategies.
Job Responsibilities (include, but not limited to the following):
- Work with Trading/Research departments in designing and formulating the model validation rules and production procedures
- Implement the rules and procedures in an object-oriented framework, via Perl, Python or
- Provide level 2 support of issues regarding model tests and production
- Collect and analyze statistics on trading models and devise approach to improve the test/production procedures
- Produce reports on models regarding their status in the validation or production process
- Design and implement user interface (web based) to interact with Trading/Research departments on model submission and validation
Job Qualifications:
- Be Interested in applying technology to real situation, comfortable working in fast paced working environment, detail oriented and capable performing task under time pressure
- Possess effective problem solving both independently or as a member of a team
- Have good communication skills: must be fluent in English, spoken and written
- Possess a background in Computer Science, Engineering, Math, Physics, with minimum  Master’s degree. Proof of a good academic record (such as GPA or other relevant test scores)
- Have experience working under Linux environment, familiar with Vi or Emacs for editing files under the command line
- Have experienced with programming in C/C++, familiar with common algorithms and data structures (binary tree, sorting, etc), Object Oriented programming and design patterns.
Familiar with compilers, debuggers under Linux (gcc, g++, gdb).
- Have experience with scripting languages, such as Perl, Python, and shell scripting
- Possess knowledge of basic statistics/probability, familiar with concepts such as   correlation, standard deviation and how to calculate
- Interface with database (such as MySQL)
Credit Risk Manager for Regulatory Risk Management
Education Level: Master's Degree or PhD Degree
Job Description:
The successful candidate will be responsible for:
·         Develop advanced credit risk capital, stress testing and loss forecasting models for banking products, and wholesale and retail credit portfolios.
·         Utilize SAS, SQL, VB, C/C++ and MS Excel to perform advanced statistical analysis and develop mathematical models to predict credit quality migration, obligor default risk, loss severity and portfolio risk. 
·         Utilize knowledge of risk assessment, risk monitoring and risk models and techniques to develop, test, document, and enhance portfolio models, visualization and diagnostic tools for testing model robustness, stability and performance. 
·         Research and analyze financial loss data, and issue special reports identifying risk drivers, estimating financial risk and measuring model risk.   
·         Perform reliability analysis and quality control of modeling datasets and model results.  Develop and maintain technical documentation for methodologies and applications; including project plans, model descriptions, mathematical derivations, data analysis, process and quality controls.  Support the implementation of analytical tools by reporting functions, and the migration of models to the production environment. 
·         Facilitate cross-functional dialogue with business and clients to improve risk reporting and internal and external regulatory compliance. 
·         Actively participate in the analysis and interpretation of results, incorporating feedback as appropriate into models and metrics. 
·         Provide timely and accurate response to clients, management and regulators.  Participate in discussions with model validation, internal and external audits and regulatory reviews. 
·         Prepare and deliver training materials, presentations and reports on credit risk analytics for technical and non-technical audiences.
·         Master’s degree in Statistics, Mathematics, Economics, Finance, Engineering or Science; and three (5) years of experience in the job offered or a related position. 
·         Professional experience must include having developed or performed the following items: 
·         Advanced mathematical and statistical financial modeling, data analysis and visualization, hypothesis testing and numerical implementation of risk models and statistical tests using SAS, SQL, VB, C/C++ and/or MS Excel tools; 
·         5 years experience in modeling banking products, risk analytics for wholesale and retail credit portfolios, survival models, credit quality migration, loss severity, credit risk or related areas; 
·         Working knowledge of credit data reliability analysis, data quality controls, processing of large datasets and databases, and data segmentation;  Project coordination across analytical, reporting, and production teams; 
·         Developing and maintaining detailed technical documentation for models, model validation and model controls, project plans and processes; 
·         Working knowledge of regulatory requirements and industry practices for credit risk models and stress testing;  Data extractions from databases and large datasets, data quality controls and integration of multiple datasets; 
·         Algorithms to process and analyze large and complex datasets utilizing SAS in Unix and Windows environments, and SQL, VB, MS Excel and/or C/C++; 
·         Statistical analysis, data modeling, hypothesis testing and data visualization;  
·         Multi-dimensional segmentation of data for modeling purposes; 
·         Model driver identification, model design, data regressions and risk model fitting;  Statistical significance test design, hypothesis testing and model performance metrics; 
·         Writing and reviewing technical model documentation for risk models; 
·         Measurement of credit risk and credit risk analysis; and Implementation, testing, validation and documentation of models for risk management purposes.   Excellent communication skills.

 To answer your questions below:
(1)     can have either Java or C++ but need to be strong in one or the other
(2)    should have experience Data Mining SAS
(3)    needs to have 3-5 years of experience as the position is VP level
(4)   should have a masters or PHD in a related field
(5)    Retail/ Econometric or credit model experience is a plus
(6)   Does not need to come from banking industry
Jason Vu
The Leverage Group
139 East 23rd Street
New York, NY  10010
Ph. 201-839-6319
permanent email:

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