1. How to find a job?
Friends who have passed the postgraduate entrance examination may have heard of the term "three crosses", that is, cross-regional, cross-professional, and cross-school postgraduate entrance examinations. Every additional "cross", the difficulty will be much higher.
Looking for a job is like taking a postgraduate entrance examination, and there are also "three crosses": cross-region, cross-industry, and cross-job.
Among them, cross-regional issues are relatively easy for young people to solve. To understand the employment market in a city, you only need to do a little research on the recruitment website.
Open the job search software, select the financial industry, and search for the keyword "data analysis", you can see many jobs. Generally, the JD (job description) of these positions looks like this:
Don't be intimidated by so many requests. Carefully analyze this job description: If the 1st and 2nd points are temporarily unsatisfactory at this stage, then it is recommended to ignore them directly, and it would be better if they are satisfied.
If you are a person who wants to change careers, don't worry about the requirements for this kind of education and related experience. You should submit your resume. HR is also human, so it will not be so rigid. If you don't vote, you really have no chance.
The skills in point 4 of the job description can be supplemented by learning, the most important of which are SQL and Python. When learning SQL and Python, the main idea is "learning + practicing". You can never learn to code by seeing.
Point 3 in the job description can be simplified as "project experience in related fields". What if there is no relevant project experience?
You can go to the Internet to find data, do your own projects, or follow related courses. There are a large amount of financial data available for free on Kaggle, Wangdaizhijia, and Alibaba Tianchi.
Some people will say that too many people have done some projects to the point of "bad work". But in fact, many projects are far from being "badly done", and at least 90% of them have not actually done any cases. So as long as you do it, you surpass most people.
2. How to write a resume?
We can consider this issue from the perspective of HR. The HR responsible for recruitment often receives a lot of resumes every day, and may only have 1 minute to read each resume.
We need to let HR match the content of the resume with the requirements of the job description in the shortest possible time, and believe that what we have written is true, clear and reliable.
The biggest headache for HR when screening resumes is that the resumes are "fuzzy". For example, "proficient in using Excel, SQL, Python".
What exactly is this "proficiency"? You can't tell from your resume at all. Everyone is proficient in writing, so who should HR notify to come for an interview?
How can I prove my skill level? The best way to do this is to "visualize" your skills. Before looking for a job, I used Excel, SQL, and Python to do projects separately, and then put the projects on my resume, so that HR can clearly know that you not only know how to do it, but also actually did it.
In this way, I quickly received a lot of interview invitations after submitting my resume.
3. How to prepare for the interview?
Job interviews related to data analysis can be broken down into the following three parts:
1) Technical basis
2) Questions about project experience
3) Business issues
First of all, during the interview, you may be asked to do some questions on the spot, so you must master all the skills required in the job description before the interview, and read more interview questions.
Secondly, any content written on the resume, especially the projects I have done, must be reviewed several times to figure out the various details.
Because the interviewer will ask very detailed questions, on the one hand to test your thinking ability and work ability, on the other hand to prevent falsification of your resume. If you don't speak clearly when talking about project experience, it will undoubtedly make the interviewer's impression of you greatly reduced.
In addition, interviewers often ask business questions in combination with their company's products. This requires you to understand the business logic of the target company before the interview.
4. Examples of interview questions
Here I can list some questions I have been asked in business interviews and my personal answers.
Interview question: How to implement row and column transposition and grouping and sorting in SQL, just talk about the idea
Answer: Use the case when statement to complete row and column transposition; use rank() over(partition by()) to complete group sorting.
For the answer, see: What should I do if the ranks and columns are swapped? Give you a universal template
Interview question: What should be paid attention to when iterating the risk control model?
A: The new model needs to be “accompanied” online for a period of time to see how well the model can predict new samples, and then conduct an A/B test to use the new model for some users and continue to use the old model for others. If it is verified that the new model can significantly improve the ability to distinguish risks, it can be fully deployed and launched.
Interview question: How to divert traffic when implementing A/B test?
Answer: There are three core ideas for implementing A/B test. One is to run multiple programs in parallel. The second is to control variables. Only one variable differs between each program. The effect must be greater than the control group to be considered significant. If only one link is used for A/B test, then the traffic between each scheme should be mutually exclusive and divided randomly, so as to ensure that the traffic of each scheme comes from the same sample space.
Interview question: Our company has a product that is a "co-branded credit card" launched in cooperation with a bank. This credit card can be used to withdraw cash. What do you think are the risks? How can these risks be reduced?
Answer: I am not very clear about the specific business process of the "co-branded card" you mentioned, so I assume that it is similar to a bank's credit card.
The difference is that your company acts as the fund provider and traffic entry, while the bank acts as the card issuer. I think there are 3 risks.
The first is the overdue risk, which generally exists in the financial field. The solution is to continuously iterate the risk control rules and retrain the model regularly to adapt to changes in the customer base. If possible, data can also be shared with cooperative banks to reduce the impact of data silos.
The second is the risk of fraud. The risk can be reduced by means of "face-to-face signing". When banks issue credit cards, they almost always ask for face-to-face interviews at offline outlets. Cooperating with banks can give full play to this advantage.
The third is policy risk. This risk point is that the bank terminates cooperation with your company out of compliance considerations.
Due to policy factors, online credit restrictions are becoming more and more stringent. In order to avoid such risks, it is necessary to increase compliance in the usual business processes. On the one hand, the interest rate is controlled within the compliance range, and on the other hand, it provides strict rules for the collection team to prohibit malicious collection.
ps: The question of this type of interview question is very critical. Its purpose is to examine whether the candidate's thinking is quick and whether he has a "feel" for the business. Many job descriptions will include a requirement: "Sensitive to data", which actually refers to such abilities.
On the premise of technical clearance, employers are more inclined to use such questions to screen candidates. When you encounter this kind of question, you can ask the interviewer more questions, and ask him to understand the specific business content before answering.
What the interviewer wants to examine is nothing more than technical foundation and data thinking. The improvement of technical ability needs to rely on continuous learning and accumulation, while the improvement of business thinking lies in thinking more, continuous learning, and not setting boundaries for oneself.
Interview Question: Is there anything you want to ask me?
A: If I am lucky enough to be hired by your company, what level do you hope I will achieve within 6 months?
Technical ability determines your lower limit, while data thinking determines your ceiling.
⬇️Click "Read the original text"
Sign up for free Data analysis training camp