How to land a Data Scientist job at your dream company - My journey to Airbnb
Data Science Weekly Interview with Kang Zhao - Associate Professor at the situation of reciprocal and bipartite social networks (e.g., dating, job seeking). Search Data Scientist jobs. Get the right Data Scientist job with company ratings & salaries. open jobs for Data Scientist. Home · Careers in Analytics; A Date with a Data Scientist My job profile at Gramener includes transformation of big data to functional data.
My goal for the final interview was to be the best version of myself and leave no regret. The interview turned out to be the best one I have ever had. They gave me the offer, and all the hard work and sleepless nights paid off. What you need is to learn in the right way, and practice many times until you are good. Take note of all the interview questions you got asked, especially those questions you failed to answer.
You should always be learning and improving. I really appreciate the help from my fellow classmates and instructors at Galvanize, everyone was very supportive and willing to help each other.
Sometimes, the result is a combination of luck and preparation, and you are just not lucky this time. I started my job search process with an interview with Uber, and I deeply regret that decision.
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Therefore, you want to make sure you are prepared before taking interviews at these companies. Narrow down what types of jobs you want to do, and what types of jobs are not for you, this will save you a lot of time. If you have ever looked at the data scientist job postings, you would know how broad the responsibilities can be. Making sure what kind of job is a good fit for you and what is not, this will help you save a ton of time in preparing for interviews.
In my case, I skipped all job postings that ask for a Ph. But I still have too many areas to learn and prepare.
Below is a summary of the resources I used during my job search. Remember, there are too many resources you can use to learn, and you can spend a lot of time just searching for the materials, please be selective and make sure you utilize them to the fullest.
Resources for data science interview preparation Statistics. A - My first research project using a real-world dataset was about collecting and analyzing data about humanitarian agencies and their networks. The scale of the data was actually "tiny" several mega bytes but the data did show us some interesting patterns on the topological similarities between different networks among these organizations e.
Kang, very interesting background and context - thank you for sharing! A - It is about the opportunity to do better prediction. With larger-scale data from more sources on how people behave in a network context becoming available, there are a lot of opportunities to apply ML algorithms to discover patterns on how people behave and predict what will happen next.
It is also possible to derive new social science theories from dynamic data through computational studies. Besides, the education component is also exciting as industry needs a workforce with data analytics skills. That's also why we at the University of Iowa have started a bachelor's program in Business Analytics and plan to roll out a Master's program in this area as well.
A - I want to better understand and predict social networks dynamics at different scales. For example, dyadic link formation at the microscopic level, the flow of information and influence at the mesoscopic level, as well as how network topologies affect network performance at the macroscopic level.
Q - What Machine Learning methods have you found most helpful? A - It really depends on the context and it is hard to find a silver bullet for all situations. I usually try several methods and settle with the one with the best performance. As for conferences, I found the following helpful for my own research: Improving our ability to make predictions is definitely very compelling!
Now, let's discuss how this applies in some of your research Q - Your recent work on developing a "Netflix style" algorithm for dating sites has received a lot of press coverage A - We try to address user recommendation for the unique situation of reciprocal and bipartite social networks e.
The idea is to recommend dating partners who a user will like and will like the user back. In other words, a recommended partner should match a user's taste, as well as attractiveness. Q - How did Machine Learning help? A - In short, we extended the classic collaborative filtering technique commonly used in item recommendation for Amazon.
How to land a Data Scientist job at your dream company — My journey to Airbnb
A - People's behaviors in approaching and responding to others can provide valuable information about their taste, attractiveness, and unattractiveness.
Our method can capture these characteristics in selecting dating partners and make better recommendations. Editor Note - If you are interested in more detail behind the approach, both Forbes' recent article and a feature in the MIT Technology Review are very insightful.
- Data Scientist at Hinge
Here are a few highlights: Recommendation Engine from MIT Tech Review - These guys have built a recommendation engine that not only assesses your tastes but also measures your attractiveness. It then uses this information to recommend potential dates most likely to reply, should you initiate contact.Data Science Connect - Ep. 2 - Nikhil Gopal, Data Scientist @ Microsoft
The dating equivalent [of the Netflix model] is to analyze the partners you have chosen to send messages to, then to find other boys or girls with a similar taste and recommend potential dates that they've contacted but who you haven't. In other words, the recommendations are of the form: The problem with this approach is that it takes no account of your attractiveness.
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If the people you contact never reply, then these recommendations are of little use. So Zhao and co add another dimension to their recommendation engine. They also analyze the replies you receive and use this to evaluate your attractiveness or unattractiveness. Obviously boys and girls who receive more replies are more attractive.
When it takes this into account, it can recommend potential dates who not only match your taste but ones who are more likely to think you attractive and therefore to reply.
Machine Learning from Forbes - "Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile," Zhao says. The research team's algorithm will eventually "learn" that while a man says he likes tall women, he keeps contacting short women, and will unilaterally change its dating recommendations to him without notice, much in the same way that Netflix's algorithm learns that you're really a closet drama devotee even though you claim to love action and sci-fi.
Finally, for more technical details, the full paper can be found here.