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:
Position: Pre-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:
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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;
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formal modeling of risk, return, and trading cost profiles for equities and other asset classes;
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maintenance and support of existing models and products;
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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:
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Ph.D. or M.S. in a quantitative field (e.g. applied mathematics,
physics, computer science, etc.) Recent graduates are encouraged to
apply.
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A strong desire to develop and integrate quantitative skills within the
required scope of designing and implementing analytic solutions.
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Ability to translate research into usable, value-added tools and information.
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Strong ability to effectively communicate quantitative topics and concepts.
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Strong written and verbal communication skills.
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Working knowledge of C/C++ a must.
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Knowledge of Perl, R/S-Plus or other scripting languages preferred
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Familiarity with Unix environment and general Unix tools preferred.
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Familiarity with SQL or other databases desired.
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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
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Main Function
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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.
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Personal Requirements
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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
C++
- 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)
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Credit Risk Manager for Regulatory Risk Management
Education Level: Master's Degree
or PhD Degree
Description
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.
Qualifications:
· 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.
