- Joined
- 7/24/18
- Messages
- 1
- Points
- 11
Hi Everyone,
Let me preface by saying this, I am a recent graduate with an arts degree so not a lot of technical skills I can boast of aside from introductory compsci courses using Python (sorting was probably most complicated topic for me), and a ML intro course based on supervised learning techniques working in R (not a fan of R btw, prefer Python, however, never used Python for data analytics).
That being said, I am keen to learn technical skills required for data analytics. While I struggled to grasp the statistical theory behind the models I learned in my ML course (ex, NN, Random Forest, GLM, among others), the application of these techniques on a real dataset (from Kaggle, cc fraud dataset) and the results made a lot of sense to me. For example, evaluation metrics such as specificity vs sensitivity, and the combination matrices allowed me to defend one model vs another.
So my actual question: I am interviewing for a position as a commodity derivatives trader. My interviewer helps his customers maintain their margins by hedging against fluctuations in price of food items. I really want to give this interview my best shot. How can I best prepare for this interview? Aside from behavioural and situational questions, there will be a technical component. How can I prepare for the latter? I am confident that I can defend my resume and cover letter (my ML course and project was not mentioned in either, so I assume they are not expecting me to be a wiz quant however, I would like to impress).
I appreciate any kind of input you may have for me.
Thanks
Let me preface by saying this, I am a recent graduate with an arts degree so not a lot of technical skills I can boast of aside from introductory compsci courses using Python (sorting was probably most complicated topic for me), and a ML intro course based on supervised learning techniques working in R (not a fan of R btw, prefer Python, however, never used Python for data analytics).
That being said, I am keen to learn technical skills required for data analytics. While I struggled to grasp the statistical theory behind the models I learned in my ML course (ex, NN, Random Forest, GLM, among others), the application of these techniques on a real dataset (from Kaggle, cc fraud dataset) and the results made a lot of sense to me. For example, evaluation metrics such as specificity vs sensitivity, and the combination matrices allowed me to defend one model vs another.
So my actual question: I am interviewing for a position as a commodity derivatives trader. My interviewer helps his customers maintain their margins by hedging against fluctuations in price of food items. I really want to give this interview my best shot. How can I best prepare for this interview? Aside from behavioural and situational questions, there will be a technical component. How can I prepare for the latter? I am confident that I can defend my resume and cover letter (my ML course and project was not mentioned in either, so I assume they are not expecting me to be a wiz quant however, I would like to impress).
I appreciate any kind of input you may have for me.
Thanks