hashemian

When I look back at the early days of using social media (Facebook, Orkut, even Instagram), it was a very positive experience: seeing recent photos of friends, planning social activities, and staying in touch with interesting people.

Now anytime I check, it is anything but positive. It's either content creators desperately trying to get your attention, or self-promoting people (whether in your circle or not) trying hard to present a different self. Even the photos from friends often appear to be a highly exaggerated version of reality, to win some sort of popularity contest.

I get part of my early social media experience these days in private chat channels in Telegram and WApp. But almost any time I spend on social media, I regret it.

And the research also suggests the negative impact of these sites is quite common. So it's not just me.

My question is, to what extent you think the current situation of social media is the result of their monetization policies, and to what extent is it the fault of human behaviour?

Our startup is planning to hire a new employee with expertise in data science. The main responsibility is to work with our existing and incoming data, and extract some meaningful information from it, create prediction and classification models to address relevant questions, and similar work.

For other positions we often have three technical interviews:

    - First one is done by an HR, involves ~10 simple short-answer technical questions, to weed out those with just a fancy CV.
    - second one focuses theoretical skills: e.g. algorithms and data structures, programming language and tools, software engineering, etc.
    - Third one focuses on practical skills: often involves one practical question that the person is supposed to resolve within one to two hours.

We are planning to follow the same pattern. For the third step, we have a good question that can show how the person thinks about the problems, how she approaches them and finds a good solution, etc.

For the first and second though, I'm wondering what questions we should include to assess the candidates? I've read previous HN posts like this one [1] and similar interview question banks, but because I don't have experience in this domain myself, I don't know which questions should be known by someone with good experience in data science, and which one is a bad one ("gotcha" question, something that is often googled, too theoretical, etc.)!

Any advice on how to pick questions for this position?

If it's any relevant, I do have CS background and a fairly good understanding of programming and software engineering. I also follow ML/AI topics out of interest, but have no practical experience there.

Reference: 1. "Data science interview questions with answers" https://news.ycombinator.com/item?id=24460141

I started data modelling as student back in 2009. I was working on a model where you could feed the contact pattern of a population and it would predict infection spread. I always believed that even a small step in this direction may have a sizeable impact.

After graduation, I realized industry is looking for a different type of data science. The goal there is not to find the fastest way to control an infection, but on how to optimize presenting ads so people buy more. The talent is not spent on using big data to understand why young people in low SES communities pick smoking, but on making political campaign platforms which measure what people love to hear, so that politicians can say the same things on the stage.

To solve this, I started Ethica Data (https://www.ethicadata.com) together with my grad school team. Our intention was to create a behavioural data science platform where the talent and tools in big data can be used to solve problems that matter to society. The focus has been on problems that one way or the other are related to human behaviour.

In this platform you can pick a project and explain why you think it’s important. Then you can ask for data and resources you need for it from your audience, and if they also feel it’s importance, they can help you out.

For example, take behaviour of schizophrenic patients. To understand this, you need data on contact and mobility pattern, and quality of social interaction from a set of schizophrenic and normal population. Then you can train machine learning algorithms to highlight early symptoms. Ethica allows you to borrow data you need from people who meet the criteria and also feel the need, and helps you with the analysis and modelling tools.

We have been working on it for past three years, and there has been some small tractions around it as well. We believe the problem is very important and sooner or later we will see some solution for it, whether from us or others.