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0 pointsapohn9y ago0 comments

>>I've been looking for a job, and I've found how vaguely organizations define their data scientist and analyst roles in their job postings really frustrating.

I lead a Data Science team and part of the struggle with writing sensible job descriptions is that there are too many people providing input into the job description. HR can also put their hand in the pot when they try to use buzzwords (e.g. Hadooop) to internally justify why a role with 2 years of experience needs to be paid like other roles (e.g. traditional Excel based Analyst) with 5-10 years of experience.

>>They tend to have a short description of the role, which is generally filled with buzzwords, followed by a list of requirements. I wish organizations would talk about what they wanted to do with their data instead.

One major challenge for Data Scientists is how hyped the role is, leading to people in an organization believing whatever they want about Data Scientists. Are you a leader who wants a business analyst who can use software and interface with IT? Data Scientist. Are you an engineering manager who wants a person who can interface with the business and use machine learning? Data Scientist. Are you a VP who thinks big data and ML is the problem to your bad or non-existent data? Data Scientist. Do you want somebody who can exhale the maximum amount of hot air while still sounding like a tech and math genius? Data Scientist.

Also add in that business people with minimal experience in modern Analytics are trying to build up Data Science and Analytics capabilities in their own part of the organization because they realize Excel is not the answer to every question. I've spent a lot of time speaking with people to help them understand the type of people they need to hire. Sometimes people are sensible and sensible job descriptions and expectations come from that. Other times they are adamant about what they need (even if they are wrong) and the end result are convoluted job descriptions that are either never filled or filled with the wrong person.

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autokad9y ago

how do you choose candidates for an interview?

I can't even get a call back for an interview lined up. I have done NLP, got a masters degree in computer science from Penn, plenty of experience with big data such as hdfs and hive, spend my free time doing what ever data science I can. but obviously doing something wrong.

any suggestions? Here is my linkedin account: https://www.linkedin.com/in/karl-dailey-02557b65

shepardrtc9y ago

May I offer a couple of quick suggestions? I've never hired for Data Scientist positions, but I've hired for plenty of other ones.

1) Change your profile picture to something serious. Get a collared shirt and a nice background outdoors. No tie. People will unconsciously judge you on your picture, so you want something that shows you're a professional, but you're confident and happy in life.

2) Think hard about your job titles. Your latest job is far more than just a "Data Analyst". It seems closer to a Scientist or Engineer role, even if your company doesn't call it that. "Analyst" makes people think of entry-level positions. Your DBA-Programmer position is more like a Software Engineer/DBA/System Admin position. If you can't pick one, generally those all-in-one positions can be known as Systems Engineers. Whatever you do, make sure that DBA isn't the primary thing people see. In general, sell your previous positions more. Like "IT". That's not a title, that's a department.

3) Expand on your projects section more.

4) Make sure your resume matches your LinkedIn. People absolutely look you up on there. I did it all the time. When the two didn't match, I was suspicious.

Good luck.

apohnOP9y ago

I've got some feedback and you might see it as mean and brutal. But what I'm doing is being honest as how I would evaluate a resume that looked like your LinkedIn profile. I'm just telling you what I'm thinking.

The first thing I did was look at your current position and I immediately became skeptical. You have been at your position for 10 months, yet you have done a lot of fancy sounding things that seem to have no connection to each other. To me this is a huge red flag that you're just grabbing some data, churning through some code you found on StackOverflow or in a book/documentation, and then making grand claims about the work you are doing at ComCast.

Your use of buzzwords only makes me think this more.

For example "Churn forecasting: probabilistic modeling of deletion rates on dVR with a beta binomial distribution to forecast the number of devices that carried a show over 300 days. Maximum Likelihood Estimation was used to derive distribution parameters."

This honestly looks like you saw a tutorial about an R-library and then you copy/pasted the documentation for one of the functions into your profile. By using so many buzzwords (including the term deletion rates), you've left me wondering if this is something you actually did or just something you made up. If you actually defined and derived the Likelihood function you should state that. Otherwise saying you used MLE means nothing since many functions in R use MLE under the hood.

Another one is "Built node.js/angularjs integrated tool to allow analysis team to test the sensitivity of KNIME workflows for forecasting." In my mind I'm thinking, what exactly does web programming have to do with KNIME? What exactly do you mean by sensitivity? How are KNIME workflows sensitive? Do you mean you are checking the accuracy of forecasting models built in KNIME? Lots of Data Scientists will have no idea what node.js and angular are and many will not care when they find out. Data Scientists may build charts using JS, but very few will be building Web based UIs themselves (I assume this is what you are trying to say?)

To be honest, I have no idea what you do in your job. Are you actually a Data Scientist as part of your job duties or did you develop an interest in Data Science and you have access to Comcast data so you've been playing around on your own?

At a hiring manager, I want to know the person I'm evaluating has spend time working on a business problem from start to finish. This means they thought about the problem, defined how to answer it (or figured out how the business owners want them to define it), figured out which data was relevant, thought through a model if it's a business problem that can be addressed by a statistical or ML method - this includes thinking through how the output of model will be used by the larger business - and making a lot of mistakes and changes in this journey.

If the person is just getting into Data Science, I want to know the person has the analytical and metacognition skills to think through a business problem. If they have that then I evaluate their thinking with models.

I'd recommend you trim down your LinkedIn profile and focus on the projects which took you some time (e.g. a month or longer) and where you had to iterate a solution. Use these projects to illustrate what you actually do at your job.

It's possible that you've done everything you state. You may be working with a team of people. At a large company you'll have other teams supporting you with data collection, cleaning, and putting the data into a shape that makes it amenable to machine learning and joining with other relevant sources. If that's the case, focus on projects where you were leading the project in some way and emphasize how you worked with and led the team.

But based on your LinkedIn profile I'm skeptical and a resume like this would not pass my filter. The HR person I work with would see your profile as impenetrable. If they can't figure out what you do and what you have done they are not going to send your resume to me.

Finally, can you move into a role at Comcast that gives you an official "Data Scientist" title? From what I've heard Comcast has a solid Data Science practice and it seems like they have some really interesting data. Finding that combination is really hard. Many good or potential Data Scientists end up at jobs where they are unhappy because the Data Science culture and/or data is awful.

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