I am currently an undergraduate junior at a target school majoring in engineering. I will be interning at a BB risk department summer (non-quant role), where I will be working on things like initial margins for non-cleared OTC derivatives. Ultimately, I am interested in more quanty risk roles. I have taken courses like statistics, probability, econometrics, data structures and etc. How am I supposed to prepare interviews for quant roles in risk department at investment banks? How much should I study programming and other stuff? These are the sorts of risk quant roles that I am very interested in: 1. CIB - Quantitative Research - Equity Derivatives – Associate – London Position: role in the Equity Derivatives Quantitative Research team - focusing on the quantitative optimization of trading Description: work in close collaboration with Flow & Exotics Equity Derivatives traders to optimize quantitatively trading operations. This involves working on risk management (Delta and Vega hedging…), derivatives portfolio optimization, systematic relative value analysis, trading signals & strategies and improving the efficiency of execution… Practically: perform research, build models, tools and processes, support the trading desk on these fields. We are looking for an Associate level quant for this versatile role which mixes classical derivatives quant skills with statistical modelling and optimization. Autonomy, good communication, strong motivation and curiosity towards derivatives trading and equity markets are critical for this role. Qualifications Skillset: • Derivatives: excellent knowledge of pricing and risk management theory (Black & Scholes…), vanilla options and volatility products (variance swaps, VIX futures and options, stochastic volatility models …) • Statistical modelling & optimization: standard techniques, machine learning. Linear, convex & conic optimization… • Strong coding background: ability to work with large amounts of data and comfortable with technology, proficient in Python and relevant quantitative packages (numpy, pandas, scikit…), good knowledge of C++ JPMorgan Chase & Co offers an exceptions benefits programme and a highly competitive compensation package. JPMorgan Chase & Co is an Equal Opportunity Employer 2. The Market Risk Quantitative Research Group at JPMorgan Chase is responsible for enhancing the VaR modeling capabilities and process as well as providing quantitative support to Market Risk end-to-end, from methodology to delivery. We partner with desk aligned Quantitative Research, Market Risk Coverage, Technology, Model Risk and Development, and Product Control teams. The MRQR team is currently looking for an associate level candidate to work on the following: • Support the BAU (Business As Usual) tasks that emerge on a daily basis for Credit VaR and SVaR for MRQR. • Generation, analysis, and automation of periodic commitments such as parameter recalculation and exposure monitoring for Credit VaR Methodologies. • Perform the analysis necessary to address Action Plans (AP) arising mostly from model review and audit groups. Qualifications • This requires a quantitative background at a Master’s level or equivalent in a hard science (maths, statistics, engineering, or science), and experience or proven interest in financial industry or model development (VaR, stress, or derivatives pricing models). • The job requires time series analysis, statistics, familiarity with VaR, Python, Excel, and SQL. • Keen interest in internal policy and governance and external regulatory rules and supervisory guidance • Strong communications skills - Verbal and Written • Team work oriented - Active collaborator and self starting individual • Strong organizational and project management skills. Risk & Control mindset • Work well under pressure with commitment to deliver under tight deadlines • Detail Oriented JPMorgan Chase is an equal opportunity and affirmative action employer Disability/Veteran.
Do you know those programming languages for the roles you want to do?
I am good at Python with the packages above(pandas, numpy, etc). Don’t know c++ but I am going to study it during the summer.
You are not qualified for position 1. Maybe position 2 is a possibility, but I don’t think you will be really competitive among the MFE types who will compete for this.
lets ride it, my pony
why you think I am not qualified for position 1?
i know options pricing models and the underlying stochastic calculus theories.
Ok, write me a PDE to price a 1y lookback option with monthly high point observations.
I’m going to have to get back to you on that one.
Sure, no problem. By the way, what is a PDE?
youre going for an internship and thats an associate role so they are clearly looking for someone with a few years working experience
“Associate” level at a place like JPM generally means that you have 3-5 years of experience OR a quantitative master’s degree or higher (I was hired into an associate role, for instance, since I had an advanced degree, even with zero experience). However, for quant roles, hiring tends to skew more towards the latter type of candidate, as most new quant hires have an MFE type degree, a PhD in a quantitative subject, or both. I have seen a couple of very talented undergraduates getting hired into true quant roles, but they are exceptional and eventually go do something else, since there is no clear career path for them in a field full of PhDs. I gave OP the benefit of a doubt in only ruling out the job that has more of a front office flavor. However, as has been stated, even the second job seems to target more credentialed applicants.
Genuine question - but how much of the quant techniques are going to be replaced by Machine Learning Algos?
What is this “machine learning” industry that people keep talking about? People keep talking about AI taking over, but in finance, a viable active investing AI that can demonstrate significant positive value doesn’t exist in common knowledge. Sure, you can automate certain processes that a human might do themselves, like reading news and buying or selling based on key phrases. But we are a long time away from a machine defining the entire problem to be solved.
Now, if you were talking about technology replacing humans for the purposes of trade execution, that is a real thing, and Job #1 above is a part of that.
+2
hi quants,
lets say u are werking on trayding sistem at fund and den laid off, can u rekreate sistem when haired at nue fund?
Machine Learning is nothing more than utilizing Data Science & Statistical techniques to make forecasts – which is already widely used within any quantitative approach to investing.
Machine learning is big and here. Electric traders are working 24/7 buy and selll , buy and sell
dont tell something we already know
people are still employed in risk management? maybe you can pull an elaine and let me know who these people are.