How to Become a Quantitative Developer?
By Chainika Thakar
A quant developer is yet another interesting role in the quantitative trading domain. There is a whole lot of difference between an algorithmic trader and a quantitative developer. While an algorithmic trader is the one who executes the trading strategies after analysing the market, a quant developer is the one behind all the programming of the algorithms. Let us find out more about the quantitative developer as this article covers:
Who is a Quant Developer?
A quant developer is a programmer who finally develops the strategies with the help of a programming language. S/he is responsible for providing financial solutions to the quantitative trading industry. Quantitative developers, thus, need to have a thorough knowledge of applied mathematics, statistical models, algorithms and scientific computing.
Quantitative developers usually work at an investment bank, a hedge fund, a brokerage firm or a financial securities firm.
Going forward, let us find out the types of quant developers.
Types of Quant Developers
Mainly, there are two types of quant developers in the financial domain:
- Front office developers
- Middle office developers
- Back office developers
Front office developers
Quant developers who work with quantitative analysts and help to implement as well as optimise the financial models. This implies getting the prototype code from a programming language such as Python and then rewriting it in another programming language such as C++ or Java. Front office quantitative developers directly interface with the clients to deliver tailor-made software and analytical tools.
Middle office developers
These are the quantitative developers who perform a middle office role at the banks. At the investment banks, this implies working on large-scale systems. They are involved in coding of the trading infrastructure which helps the quantitative analysts or traders in running or executing their models on.
Back office developers
These developers are the ones who perform the role at the back office for tasks such as model validation and risk management. For this role, you need to have the theoretical knowledge/skills, analytical capabilities and a deep approach to a problem. Model validation helps with solving the problems at the back end so as to avoid any technical problems further.
Next, we will find out what qualification helps to become a quant developer.
Educational Qualification to Become a Quant Developer
As we discussed in the definition of a quantitative developer, it is the mix of finance, computer science, applied mathematics, statistical models, algorithms and scientific computing which a quant developer requires to be knowledgeable in.
An aspiring quant developer must learn a coding language, especially out of the most common ones, such as Python, C++, C#, R and Java.
Also, the knowledge of tools is required to become a quant developer since these tools help with backtesting and finalising the strategy for trading. Some of the general tools/platforms are discussed below in the article.
Moreover, Quantitative Finance is another subject which provides the knowledge required to analyze financial markets and securities. This analysis is basically done by using mathematical models and huge datasets, hence, the specialists in this field are known as quantitative analysts.
Nevertheless, a quantitative developer gets much better at the job if he/she possesses the knowledge regarding the financial markets and pricing securities so as to understand what to code for the analyst.
For instance, if the quantitative analyst comes up with a bull-market or bear-market strategy, the quant developer must have the knowledge of the intricacies of markets as well as the prediction/pricing practice of the securities.
Hence, quant developers need not have their own strategies. They simply need to understand and adopt strategies to perfectly automate them.
Moreover, it is equally essential to possess the knowledge of risk analytics for the management of risks which most of the financial models are subjected to.
Hence, it is really crucial to acquire the knowledge of using quant models that help the analysts to analyse past data, current as well as anticipated data for the future.
Let us now find out the job description for a quantitative developer.
Skills/Job Description Required as a Quantitative Developer
As a quantitative developer, the role that you take up is not only challenging but also exciting if you are into programming wholeheartedly.
Also, in order to become a quantitative developer, you must know how to go about it from honing the basic skills to mastering the advanced ones.
Below, I have mentioned all these skills as follows:
- Software development
- Communication skills
- Theoretical/technical skills
- Programming skills
- Machine learning and deep learning
Software development skill is one of the most important since it is an extremely important skill as a developer since it helps you understand the core of the development role. Moreover, automation requires a good knowledge of software development, this skill is highly appreciated in the finance domain as well. With all the development in your job profile, mastering the software development skill adds to the understanding of the core of the work.
It is extremely important that a quantitative developer maintains good communication skills/soft skills so as to be able to communicate well with the analysts and others for programming well. Knowing how to use the soft skills is a plus for any quantitative developer since it increases the efficiency and makes any quantitative developer successful.
As a quantitative developer, you would be needing to develop and maintain the quant models for several functions at the investment bank or financial services institutions. These functions are pricing, risk management as well as analysis. To develop such skills and for creating these models, you will need strong knowledge in the statistical and mathematical domain. Some of these models which are deployed for quantitative development are:
- Consolidation model
- Option pricing model
- Forecasting model
- Discounted Cash Flow (DCF) model
- Merger model
This model helps with consolidation of multiple business units into one model
Option pricing model
This model is on the basis of theories like Black-Scholes and binomial tree in trading
This model helps to forecast the pattern of a stock and make right decisions with regard to investing in the stock market
Discounted Cash Flow (DCF) model
This model happens to be important for calculating net present value and future values in the market
Also known as merger and acquisition model, it is computed with the primary merging companies
Next step is to gear yourself up by familiarizing yourself with efficient algorithms and the knowledge of programming laguages such as C/C++, Python, Java and, MATLAB. This will equip you to bea quant developer in the financial industry.
Programming skills are the most important for a quant developer and if you have all other skills but not a master at programming, then you can upskill anytime with the help of online learning programmes.
Machine learning and deep learning
With the machine learning and deep learning knowledge, you will be able to implementlogistic regression models, neural networks, LSTM among other ML techniques in the finance industry. Moreover, the machine learning technology is advancing and making it convenient for the financial domain at every step. With time, the advancement of the technology and knowledge helps a quantitative developer with its role on professional level.
To give you an instance, for the position of the quantitative developer at iRageCapital, here are the job responsibilities:
- The developer will work with our quant research/analyst team and will assist in projects to improve stock selection, portfolio construction or implementation
- Main goals for this person would be around developing internal research platform
- Strong coding and data management capabilities
- Ideal candidates would have basic knowledge of accounting and finance
- SQL capabilities and understanding of relational databases and intermediate knowledge of finance/accounting
- Learnability, teamwork and flexibility are important traits we look for
Moreover, following are the tools that the quantitative developers use to develop the financial models and the trading strategies:
Now, we will take a look at the salary/compensation for a quant developer.
A quantitative developer’s financial reward/salary is tremendous and it is so because the job of a developer involves everything from understanding markets to coding.
Below, we have arranged a list of average salaries/compensation for the role of a quantitative developer in different countries:
Average Base Salary/year
Although the abovementioned salaries are just a representation of average base salary for a quant developer as a fresher in each country. Whereas, a professional quantitative developer can earn almost around $2,50,000 a year excluding bonuses. With the bonuses added, the salary even goes up to $500,000 per year for a successful quantitative developer.
Now, let us see what steps you can take as an aspiring quant developer.
Steps to Become a Quantitative Developer
As now you have gone through the skills required for this role and the salary/reward to the quantitative developer, now you can see the steps to become a quantitative developer. In this subtopic, you will find out how to map the skills with the requirements of the industry. These important steps are:
- Identifying and developing the gaps in skills
- Shortlisting career opportunities
- Preparing for the interview
- Professional development
Identifying and developing the gaps in skills
First of all, as an aspiring quant developer, you need to find out the gaps in skills or such skills which are missing in your path. As you read in the above section, you need some technical, programming and software related skills as well as communication skills. Best is to find out which skills are needed to be picked and covered in order to avoid any hindrance.
For instance, if you are from financial market background and never could work on any technical skills, then you must opt for the courses which could provide you with the expertise.
Since it is extremely crucial to build upon these skills, you must enrol yourself in a course specializing in that particular subject which you need.
You can avail the benefits from courses offered online such as Machine learning and deep learning in financial markets, Automated trading using Python and Quantitative approach in options trading.
Shortlisting career opportunities
Secondly, you must shortlist the career opportunities available so that you can find out the one that deems you suitable. After shortlisting, you can begin applying for the quantitative developer role in the companies you prefer. Here is a list of top companies that hires quantitative developer:
Preparing for the interview
Preparing for the interview is the next step to look for when aspiring to become a quant developer. It is highly recommended that you take the professional help by gaining knowledge from an existing quant developer or enrol in a course such as quant interview questions preparation. It is much better if you prepare yourself with a mix of tricky interview questions for cracking the quant interview.
Last but not least is the professional development which implies keeping yourself updated all the time even after you have a job that you can rely on. Keeping up with the new opportunities and finding ways to better yourself professionally will help you enhance your capabilities. Also, you will be able to contribute exceptionally well to the company you are working for.
A quantitative developer’s role is spread across the application of several subjects such as mathematics, statistical models, algorithms and scientific computing. Since a quantitative developer is required to code and automate the strategies for the analysts, a developer must have all the knowledge of securities and financial markets as well. You must also have a good skillset for cracking the interview at a bank or a hedge fund for the role of quantitative developer.
All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.
Know About Quantitative Developer
Who is a Quantitative Developer? What does the Quantitative Developer do? How do quants program a financial product? Do these questions sound familiar to you, or do you find it different? Whether it is familiar or unique here, we are going to give you a competent thought fire on how do quantitative developers do code the complex financial models and derivative pricings?.
This article is all about Quants, quantitative researchers, quantitative developers and quantitative analysts who develop crafty solutions to trade in the financial markets to strike strong earnings.
Who is a Quant Developer?
A quant is a computer programmer who develops financial modeling solutions to quantitative finance and quantitative trading industry. Quantitative developers would have profound knowledge of applied mathematics, statistical models, advance finance concepts, data structures, algorithms, and scientific computing.
Quantitative developers are more in demand across investment banks, hedge fund companies, asset management companies, trade brokerage firms, and financial securities firms.
Quants provide simple solutions to more complex trading problems through deep quantitative analysis with mathematics computations. Quantitative developers create mathematical models with the key components of trading such as price and volume of the stock.
These are about the quantitative developers, coders who are familiar with Java, C#, C++ could foray into investment banks as Quants. Whereas coders who are sound with R, Python, Matlab can catch up with the hedge fund firms and brokerage firms.
Prerequisites for Implementing Quantitative Models
Any computer science engineer is highly competent to enter into the quantitative finance and trading field. Where computers and electronic transaction plays a vital role in today’s scenario and stock exchange is no getaway. Along with the skills of a computer engineer, there are some industry-ready skills a quant should be sound of. They are highly proficient in database management systems, statistical analysis software like Matlab, SAS, R, S-Plus, advanced spreadsheet skills, data structures, algorithms, and coding in C++ more prominently or Python, Java, SQL.
So if you have a thirst for numbers and curious to develop financial models then Quantitative finance is a way to go option. The implementation of Quantitative models is a broad area like algorithmic strategies, quantitative financial models also have numerous strategies that do have to be computed with analyzation of past data, current and anticipated future data.
Programming Languages in developing Quant Models
Advanced C++ concepts are of prime importance for developing financial instruments and derivative pricing, whereas Java, Python can be utilized for algorithmic high-frequency trading purposes.
Knowledge of advanced statistical analysis software like Matlab, R, SAS would be a vital plus. If you got the courage and passion for trading, provability and financial models one could become a self-taught quantitative developer. You just need to have a ton of practice on the algorithms and financial numerical models.
Tools For Quants: Let’s see about Quant software tools that help in creating a financial model or trading strategy. Quant software for Derivatives pricing in financial products and Management of complex Risks are:
- Quant Lib: These are library functions are written in C++ exclusively for derivative pricing.
- JQuantLib: Same as Quantlib but written in Java
- OpenGamma: For analyzing operational risk and market risk
- Maygard: For evaluation of stock and price movements
- Quantcode: For financial modeling
- Rosetta code: To practice on quantitative programming.
- Nvidia Computational Finance Tools: For financial modeling and graphical simulation purposes.
Software for Trading Purposes
- Quantopian: For hedge fund backtesting algorithmic trading strategies. Written in python also used with brokerage agencies for paper trading. They conduct algorithmic trading contests too.
- Quantconnect: Forex algorithmic trade engine used for backtesting.
- Quantmod To develop, test and deploy trade engines.
- Quandl: Provides data sets and models for deploying in quantity.
Financial Models in Practice: Let’s look into what are the most familiar mathematical and statistical models that are being deployed on quantitative development.
- Sum of the Parts Model: This model is computed by taking multiple discounted cash flow models and adding them finally.
- Consolidation Model: Here multiple business units are consolidated to one model.
- Budget Model: Focusing the income stream the budget models are framed.
- Forecasting Model: Predicting the future expense with the financial planning and analysis.
- Option Pricing Model: This is a straight mathematical model based on the binomial tree and Black-Scholes.
- Three Statement Model: The three important financial statements income statement, balance sheet statement, cash flow statement is formulated in Excel macros.
- Discounted Cash Flow (DCF) Model: This is computed along with three primary statements along with net present value and future values.
- Merger Model (M&A): This is the merger and acquisition model computed with the consolidation of primary states of merging companies.
- Initial Public Offering (IPO) Model: With a thorough company analysis the IPO models are constructed.
- Leveraged Buyout (LBO) Model: Based on debt schedules this model is constructed.
Quants are considered to be quite tough as they involve high-risk management and financial product modeling that requires a deep analysis of the economic spread of the demographics. But it’s a challenging career if you got the passion towards mathematics models, excel macros, and programming it’s a way to go option.
Quant Developers (what is it like?)
The standard salary for a first year analyst is $65K + $10K signing bonus at a bulge bracket in New York plus some sort of bonus in June-August of the following year. With a master's degree, you would typically accrue one year of experience and earn $70-$75K. Bear in mind that $65K/year doesn't go as far as you would think after NYC taxes, rent, and cost of living.
Are the salary index based on entry level, 2 - 3 years experience, 3- 5 years, more than 5 years? And what's the percentage of the guaranteed bonus on average?
Thank you, all!
Click to expand...
Guaranteed bonuses are usually reserved for experienced transfers. When you transfer firms, you usually lose a whole lot (including some hard-to-measure accrued bonus, unvested stock options, and unvested 401k matches.) Your new firm will often match that if they want you badly enough. Since most college grads aren't as sure a bet as experienced hires, it is unlikely that you will need to worry about guaranteed bonus if you are straight out of school or even if you have been working for four or five years. Only if you've been working for a certain period of time and you are losing a lot of bonus (IE: you transfer in November) and the firm really wants you or the firm thinks you're a "sure thing" will you need to worry about guaranteed bonuses.
I've recently received many emails from individuals wishing to change careers from engineering, academia and IT. Quantitative finance has become a particularly attractive area recently due to the intellectual challenge and high remuneration. A question that constantly arises in these emails is "what do I need to study to convince an interviewer to give me a job?". This is the first in a three-part series that will discuss how to form a self-study plan to gain a job in the quantitative finance industry. This post will discuss how to self-study to become a quantitative developer. The other two will concentrate on quantitative analysts and quantitative traders.
Any career in quantitative finance requires a degree of generalisation rather than extensive specialisation. Quantitative developers are no different. They must fit into a team of traders, financial engineers and IT support in order to help investment banks price and sell new structured investment products or help funds develop trading infrastructure and portfolio management systems.
The most common route into quantitative development is via an academic background in scientific computing. This is because the core skills necessary for a "quant dev" are advanced programming skills and numerical algorithm implementation. These skills are developed as a matter of course within a grad school research environment for the physical sciences or engineering. If this is your background then your task will be to get to grips with the specific products and numerical algorithms used within quantitative finance, as your general implementation and programming skills are likely to be sufficiently developed.
However, if your background is not in scientific computing, there are still plenty of opportunities to become a quantitative developer leveraging a background in programming. At the very least though you will need to be familiar with implementing algorithms, the practice of which I will discuss below.
First and foremost a quantitative developer IS a software developer. Thus the role will almost exclusively be 100% programming based. You will find yourself optimising trading prototypes or developing trading infrastructure from scratch. If you're targeting bank roles, then you will likely need to be using C++, Java or C# in a Microsoft/Windows environment. If you are targeting hedge funds then you will likely be translating MatLab or R into C++ and/or Python. Funds tend to use Java and C# less, since they're often in a UNIX environment where C++ and Python make more sense. If you have a background in either of these programming environments, it makes sense to develop your strengths and stick with software you know well. Thus if you know Java, for instance, it would be wise to target investment banking roles. I've written an article on programming languages for quant developer roles if you want more detail.
Assuming you are a competent programmer and are happy changing to the software most widely used in the financial industry, then I would suggest learning C++ and Python extremely well, as that gives you the most cross-sectional capability across different sectors of the industry. Here is my suggested study plan to become a good C++ programmer:
- Read through the entirety of Accelerated C++ by Andrew Koenig. This book will get you up to scratch on C++ syntax and, in particular, will go into detail about pointers and memory management. This is an area that many programmers (including those from a Java and C#) background will not be as familiar with. It is best to read through while implementing the examples, either with Microsoft Visual Studio or the GCC compiler on Linux/UNIX, in order to practice the syntax, rather than just reading it!
- Read through (multiple times!) the entirety of Effective C++ by Scott Meyers. I've repeated this advice consistently across the site, but it still bears saying again. This book will take you from a beginner C++ programmer to an intermediate programmer who is ready for interview. This book is harder to actually practice in front of the terminal, as Meyers sets up some complicated examples. One way of running through this book would be to determine where in your own projects you can apply the patterns.
- If you are applying for C++ jobs directly, you will probably want to go beyond these two works. Scott Meyers has also written More Effective C++ and Effective STL. You will then need to consider the Boost library, multithreaded programming and Linux operating system fundamentals to become a true expert.
Similarly for Python:
- If you are already a skilled C++/Java/C# programmer, then read through certain sections of Learning Python by Mark Lutz. In particular, skim Chapters 4-9, which discuss Python's built in types. Chapters 10-13 discuss Python's syntax for branching and looping, so they can also be lightly read to determine syntax. However, it is worth spending significant time on Chapters 14-20, as they discuss Python specific features such as Iterations/Comprehensions and advanced function usage. Sections V and VI cover namespacing and object-oriented programming, and how these concepts differ in Python from a language such as C++/Java. If you want to be a good intermediate Python programmer, then you should also consider the remaining chapters in the book. Remember though that this book is over 1,000 pages long, so you will need to pick and choose the sections relevant to your background.
- Mark Lutz's second book, Programming Python, is on actually building applications within Python. This will take all of the syntax knowledge gained in the first and allow you to start building robust applications. This will help you become a much better Python software engineer.
- For those who are definitely keen on the quantitative trading side of the industry, it will be necessary to learn how to carry out data analysis within Python. This is a skill often picked-up while in grad school, but Python for Data Analysis by Wes McKinney nicely covers some of the newer libraries available such as SciPy and pandas.
After following the above plan you should have a good chance at any C++ or Python interview. However, in order to solidify your developer skills it is necessary to be aware of some of the recent innovations in software engineering, which only tend to be figured out "on the job", but can certainly be studied and practiced at home in your spare time.
Being a good interview candidate for a quant developer position requires that you become both a good programmer AND a good software developer. Many can learn the former from textbooks and practice. However the latter can only be learnt from working on larger software projects, generally with other developers. However, this does not mean it cannot be incorporated into a home study program! For instance, it is now easy to contribute to open source software projects via the internet. One of the largest quantitative finance projects is the project. Reading through (some of) the source code to this project will give you a good idea at how large-scale C++ software projects are written.
To become a good software developer it is necessary to understand how to craft large-scale software projects. For modern software development this requires using version control, continuous integration and other agile practices. Here is a study plan to help you get to grips with these concepts:
- Read through both Steve McConnell's Code Complete and Robert Martin's Clean Code. Both of these books will cause you to seriously rethink how you go about designing software, from first principles. For instance - how much time do you spend upfront designing your software before touching the keyboard? Both of these books will save you hours of wasted code development. I would suggest applying as many of the tips within these books to your projects as soon as possible to remove any bad habits. They are also great to discuss at interview, as you'll invariably be asked to write some code.
- A discussion on good software craftsmanship would not be complete without mentioning Design Patterns, also known as the "Gang Of Four" book. This book is highly relevant for a language such as C++, although less so for a scripting language such as Python. You will likely notice that you are using similar designs in your code. This book helps you determine when and where they should be applied. One benefit is that other good developers will be aware of them - making your more popular among your development peer group. It is quite a hard read for self-study, so try and pick 2-3 designs such as the Factory, Decorator or Singleton that are used most often and then work your way through from there.
- When working on large-scale software projects with multiple team members it is an absolute necessity to make use of version control software. Automatic revisioning, rollback, branching/merging and better testing capability means that version control is ubiquitous in nearly all (good) software institutions. The two big contenders are Git and Subversion (SVN). I would suggest becoming familiar only with Git as SVN is similar (if a little harder to use!) and most institutions are replacing their SVN repositories with Git equivalents. There is a free eBook on learning Git, Pro Git, which I suggest you work your way through. It will save you hours of wasted development time!
- Some industries are now turning towards continuous integration practices, which encourage continuous testing and deployment of code via a fully automated testing and deployment system. Although you are likely to be able to pick up most of how a CI system works "on the job", you might want to impress your interviewer by demonstrating your knowledge of the subject via working through a book such as Paul Duvall's Continuous Integration.
Although becoming an excellent programmer and an excellent software developer are the prerequisites to gaining an interview position, you will also be asked problems relating to data storage and analysis. One of the key components in a quant dev's day to day life is interacting with databases. Thus a certain level of maturity with database handling is to be expected. If you have never utilised a data storage system, then the best way to start is by beginning to understand Relational Database Management Systems (RDBMS) and their language - Structured Query Language (SQL). Common RDBMS' include Microsoft SQL Server, Oracle and MySQL. Other types of data store systems include the so-called NoSQL data stores, including 10Gen's MongoDB and Cassandra.
The best way to begin learning about RDBMS is to install an open source version (as you can download them for free!) and follow the reading list below. It is beyond the scope of this article to teach you how to install an RDBMS, but you can try MySQL, as this is a very common database within hedge funds. SQL Server and Oracle are more likely to be prevalent within banking. Once you have installed a database such as MySQL, use the following guides to help you understand storage and access of data:
- If you have no familiarity with SQL then the O'Reilly book Learning SQL by Alan Beaulieu is a great start. It covers all of the beginner and intermediate SQL you will need to know to store, access and provide reports for data. It will discuss database optimisation in a brief way as well. Make sure you read the entire book as all of the material is relevant for day to day quant dev database duties. For specific database tasks, you will want to have a look at the O'Reilly SQL Cookbook. I found this book incredibly useful when I was a quant dev, as I was continually pulling it off the shelf to look up a certain date/time or reporting query! There's no need to read this cover-to-cover, but certainly having an overview of the contents and where to look the material up is useful.
- Although quant devs are not often database administrators, if you wish to learn more about advanced MySQL optimisation, then the following two books, while certainly not necessary, are highly useful if you are running into database problems: High Performance MySQL and MySQL High Availability.
Finance and Numerical Algorithms
Since a quantitative developer works in the financial markets, it is useful to have a relatively good understanding of the products that banks produce or the instruments that funds will be trading. Thus it will be necessary to familiarise yourself (broadly) with the equities, forex, fixed income, commodities and related derivatives markets. In particular you want to be continually thinking about how this data is represented, stored and accessed as a big part of a quant dev's job is to provide storage and access to financial data. Once in the job you will almost certainly concentrate on one particular area in depth, so make sure your initial research is quite broad.
Of more relevance are the algorithms used in quantitative finance to carry out both instrument pricing and algorithmic trading. The investment bank derivatives pricing techniques will almost certainly concentrate on Monte Carlo Methods and Finite Difference Methods, both of which rely on knowledge of probability, statistics, numerical analysis and partial differential equations. These are all topics which a good student will be familiar with in grad school, but for those considering a career change, you will need to gain a good understanding of these methods if you wish to become an options pricing quant developer in a bank.
For hedge funds, you will likely be implementing trading infrastructure - either low or high frequency. This will involve taking an algorithm already coded up in MatLab, R or Python (or even C++) and then optimising it in a faster language, such as C++, as well as hooking up this algorithm to a prime brokerage application programming interface (API) and executing trades. The skills required here are quite disparate. You will need to be able to pull together data from various sources, put it into the correct context, iterate over it rapidly and then generate on-demand reports either in fixed-format (PDF), over the web or as an API itself. These skills are hard to learn from books directly and require a few years of software development experience in the technology industry.
In order to read about these topics further, please have a look at my C++ Implementation articles, my Python Implementation articles and the Quantitative Finance Reading List.
Applying for Jobs
Although the above list looks like an extensive amount of material to study, this will only be the case for somebody completely new to programming. It is unlikely that a quantitative developer position would be suitable for such an individual and I assuming that your own background will be in programming or the physical sciences. Make sure to read only the sections you deem relevant to your own situation, as otherwise you could easily spend a few years of your spare time learning the above material!
Once you believe you are ready for interview then you will need to begin the process of contacting quantitative recruiters. There are specialist firms that deal with investment banks and hedge funds. If you require specific names, then feel free to email us at [email protected] and we will happily point you in the right direction.
Any good recruiter will discuss your background to a reasonable degree of detail as they are putting their reputation at stake when they recommend you for an interview. Recruiters aren't generally highly familiar with the technicalities of quantitative technology and nor do they need to be. However, this does mean they have to rely more on "buzzword matching" for their own CV/resume filtering. Make sure if you are strong with C++ that you state "C++ skills - strong" and reference the STL, Boost and any C++ projects you have worked on, for instance. Do not be modest about your skills, but also do not overstate them. If you write anything on your CV/resume, it is fair game to be grilled about it in a technical interview!
Since the job market (in 2013) is not the best (particularly at entry-level) right now, you might find it will take a while to get the job you are looking for. The trick is to keep trying as with each interview you attend, you'll gain more knowledge about what the recruiters and interviewers are looking for and so you'll be able to tailor your study towards this.
If you have any questions at all about becoming a quant developer at all please take a look at this article on my own experiences as a quant developer or email us at [email protected]
.Coding Won't Make You a Quant
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