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How should I prepare the interview for a quantitative researcher in a ML/AI team?

Personal Background:
  • Graduate Study: I am now a 1st-year student in the Master of Information System and Data Science program at USA and I will graduate in Dec 2020.
  • Undergraduate Study: Major in Financial Engineering in China. Exchanged in the USA for one semester and studied Data Science courses.
  • Work Experience: No full-time work experience.
  • Internship: Algorithm Engineer in a fintech company for 6 months; Quantitative researcher in a futures company for 2 months; Stock Analyst in a fund company for 3 months. All of these internships are in China.
Career Goal:
I am interested in using machine learning algorithms and AI technoloies to develop strategy in trading financial products, especially stocks and options. So I want to apply the quantitative researcher postition in a Machine Learning or an AI team for summer internship as well as full-time job in the next year.

  1. What about the demand of such a position?
  2. Do employers in these team only hire phd stuents?
  3. How should I prepare for such a position (especially in the aspect of technical skills)? Do I need to do exercises in Leetcode?
  4. What is the importance ranking of the following skills (what is important and what is not important):
    a) machine learning algorithms;
    b) deep learning algorithms;
    c) data structures and algorithms;
    d) coding proficiency of C++ or Java;
    e) coding proficiency of Python or R;
    f) coding proficiency of shell;
    g) finanical knowledge, like derivatives pricing;
    h) mathematical knowledge, like prob, stats and stochastic process (what covered in A Practical Guide to Quantitative Finance Interviews by Xinfeng Zhou);
    i) big data tools, like spark and hadoop;
    g) SQL database, like oracle and SqlServer;
    k) NoSQL database, like MongoDB;
    h) others. (Please specify.)
  5. Although I learned some machine learning and data mining courses before, what I have mastered is not deep enough. But I will study deeply for machine learning, deep learning, big data and data structures in the following year at my graduate program. So if I apply for a summer internship in a company but fail to get it in this semester, can I apply this internship in this company again in the next semerster?

Since most of the alumni of my graduate program do not focus on financial investment field, I know few people that can give and suggestions toward these questions. And I have been beset with these questions for a long time. Sincerely wish career advises in Quantnet. I would be greatly appreciated if someone here can give me suggestions or share some experience with me. Thank you very much.
Coding, algorithms, ML and statistics all equally important.
Data tools not really tested in interviews.
Specific language not as important
Finance knowledge not required for most shops
Deep learning less important unless its specifically a DL shop. You'd be surprised how many places are actually usually DL successfully (hint: its not many)