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
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
in finance, demonstrated by coursework in economics or finance
skill (C++/Python/Perl) is a strong plus
-Ability to work
independently and innovate
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
-Strong work ethic
and drive to succeed
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:
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
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
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
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.
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.
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.
Degree in Mathematics, Engineering or Computer Science
years C/C++ development experience on Unix/Linux
years knowledge of equity trading and financial market data interfaces
years understanding of code optimization and high performing trading, low latency applications
-Advanced degree in Mathematics, Physics, Engineering or Computer Science
Player capable of developing industrial, high performance strength software components
of developing financial market interfaces or market data handlers
knowledge and understanding of systematic algorithmic trading
to convert quantitative algorithm trading design into code
equities and futures market microstructure
of developing against US equities and futures APIs
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
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
- 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
The successful candidate will be responsible for:
advanced credit risk capital, stress testing and loss forecasting
models for banking products, and wholesale and retail credit portfolios.
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.
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.
and analyze financial loss data, and issue special reports identifying
risk drivers, estimating financial risk and measuring model risk.
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
cross-functional dialogue with business and clients to improve risk reporting and internal and external regulatory compliance.
participate in the analysis and interpretation of results, incorporating feedback as appropriate into models and metrics.
timely and accurate response to clients, management and regulators.
Participate in discussions with model validation, internal and external
audits and regulatory reviews.
and deliver training materials, presentations and reports on credit risk analytics for technical and non-technical audiences.
degree in Statistics, Mathematics, Economics, Finance, Engineering or
Science; and three (5) years of experience in the job offered or a
experience must include having developed or performed the following items:
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
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;
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
and maintaining detailed technical documentation for models, model validation and model controls, project plans and processes;
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
to process and analyze large and complex datasets utilizing SAS in Unix
and Windows environments, and SQL, VB, MS Excel and/or C/C++;
analysis, data modeling, hypothesis testing and data visualization;
segmentation of data for modeling purposes;
driver identification, model design, data regressions and risk model
fitting; Statistical significance test design, hypothesis testing and
model performance metrics;
and reviewing technical model documentation for risk models;
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:
have either Java or C++ but need to be strong in one or the other
have experience Data Mining SAS
to have 3-5 years of experience as the position is VP level
have a masters or PHD in a related field
Econometric or credit model experience is a plus
As per the course selection policy, enrollment caps for all CS courses have been returned to their actual class size. If the class or section is full then it means that you will need to wait for another student to drop it. You have until 11.59 p.m. on Monday May 14 to add courses.
The following courses still have waiting lists and department consent applied so you will NOT be able to add these courses.
Appointments for course enrollment began on Monday 26 March and open enrollment begins on Wednesday 28 March.
For students trying to add CS courses, we encourage you to review the course selection page.
COURSE ENROLLMENT In Computer Science, we drop the enrolment caps by approximately 15% of the total enrolment number for the course to give advisors room to handle special cases. We will return the enrolment caps to their actual size in the second week of classes after we have had time to deal with the special cases. This will occur on Friday, May 4, 2018, (we don't know the precise time) and remaining space will be available on a first-come-first-served basis only if the section isn't already at its enrolment total. WHAT IS A SPECIAL CASE? 1. Students who selected courses but something went wrong because of: enrollment capacity in a course **a time conflict **an academic enrolment block was applied after course selectiondropping or failing a course after course selection** Please re…
The CS Undergraduate Advising Office has opened applications for CS transfers for Fall 2017. The online application is available at https://oat.uwaterloo.ca/formsand will be open from Tuesday, December 5 until Sunday, December 31, 2017, at 11.59 p.m.
We're accepting applications for:
Transfer from Math to CS - at a minimum, must have taken CS 136 or be taking it in Fall 2017.Transfer from CS to BCS (Data Science) - must have taken STAT 231 or be taking it in Fall 2017.Transfer from CS to CS/Digital HardwareAdding a Joint CS to an out-of-faculty planTransfer from outside of Math to CS (pending approval from Math)Answers to common questions about the transfer process can be found in the CS FAQs (particularly #2 and #151).