Career that intersect Data Science and Quant Finance

[dhruvk]

Hi all,

I have been following this forum for about 2-3 months and recently join Quantnet as a member.

My background and profile till now:
1) UG in Computer Science from a tier 2 university in India with CGPA 8.1/10(exact)
2) Did couple of mini projects and 1 major final year project related to data analysis and finance.
3) Did 2 internships: 1 in finance(3rd year summer vacation) and 1 in data analysis( 2nd year summer vacation)
4) Did few introductory finance courses and machine learning courses on coursera and edx.
5) Relevant courses during UG: Maths 1&2, C++ in 2 semesters and some economics courses.
6) Currently, working as a developer. My work includes Python programming and MS-Excel.
7) Pro-Bono consultant at Statistics Without Borders.

I'm thinking to apply for a MFE program where I could take majority of quant courses and few data science courses. I know the above profile is very average and won't going to help me placed in top MFE programs. So, I'm going to do few things before the applications work to improve my profile and make it more tailored for top MFE's progarm at USA:
1) Complete all necessary maths courses from resources like MIT OCW and khan academy. As far as I know, there is no provision in India to go back to a local university and take maths courses.Correct me if I'm wrong.
2) Brush up my C++ knowledge from my UG courses and refer some books to learn more. Also, learn R, VBA, MATLAB.
3) Complete CFA level 1,2 before heading to states.(I'm a level 1 candidate)
4) Refer the master book list of quantnet to gain in-depth knowledge.
5) Do some relevant competitions and hackathons on kaggle platform.

My queries are:
1)Is there any career that includes both data science( especially machine learning and modelling) and quant finance? I'm inclined towards both data science and quant finance and would like to dedicate rest of my life to a career that intersect these two.
2)Is there any type of quant profile who works in data science?
3)Are the above listed things enough to reach my goal? If not, any suggestion would be highly appriciated.

Thanks,
Dhruv Kathait

 

[Connor Whelan]

Yeah "Data Science" is used a lot in Quant Finance. I went to a conference in Boston where one of the speakers who works at Two Sigma had a Phd in CS focused on Machine Learning, and another speaker who works at Morgan Stanley had a Phd in Mathematics. Also, a Quant I used to work who got his MS Comp Finance from CMU now works as a Data Scientist for a tech company. Quant Finance seems to be a pretty broad field with lots of different disciplines. A Quant at a hedge fund may be doing different work than a Quant at a bank, plus there's multiple different Quant roles within firms.

 

[dhruvk]

@Connor Whelan Thanks a lot for sharing your knowledge.

 

[dhruvk]

@Andy Nguyen @Daniel Duffy @pingu @Convoxity @Ken Abbott I would really be thankful if you provide your opinion.

Thanks

 

[Mathchismo]

@Andy Nguyen @Daniel Duffy @pingu @Convoxity @Ken Abbott I would really be thankful if you provide your opinion.

Thanks

The use cases will be fundamentally different to those from your traditional quants.

Two Data Scientists will be completely different and they probably won't do the work you think they do.

Example Data Scientist use cases:

1. Operations - Email and trade data classification (NLP): Do you want to focus on NLP for fraud detection, AML, helping classify documents ?
2. Predictive modelling: This could be for equities, it could be for insurance risk models - normally however you would have to compare your Machine Learning algorithms (i.e. RNN's) with traditional time series based forecasting techniques. Thus you probably still need to have a quant background if you want to be in this space. This can also be done for real estate valuation for M&A deals.
3. Big Data Analysis: Analysis large data-sets - looking for for correlations, and insights over specific time period. Automating data analysis.
4. Retail Finance: Consumer customer lifetime value modelling, Machine Learning driven marketing
5. Trading: Reinforcement Learning for live trading of equities (often requires a PhD or deep specialisation)
6. Machine Learning Engineer: Software Engineer who builds software products for the aforementioned use cases.

As you can see it is fairly diverse.

If you have a quant finance/financial engineering background then you could end up being a Portfolio Manager assuming you geared your skill-set towards number 2 - Predictive modelling.

If you are a Natural Language Processing expert that is a completely different job and probably not what you imagine.