Small aaeaaqaaaaaaaamcaaaajgy0yjhimti1ltg1m2ytndq2os04zddjlwniogy2zte2ymzhnq Ajay Sharma on

I'm a data scientist from SF who relocated to NYC this spring. I prudently spent the prior 8 months scoping & planning, making sure there was a healthy appetite for data scientists in the region. But when I got here it didn't seem like I was getting the responses to my outreach I had anticipated ...

Why is No One Getting Back to Me?

I was a little skeptical the slow-start was attributed to just my own performance and the typical nature of a job search. From what I could tell, the pool of actively open jobs was quite shallow. Eagerly searching for an explanation, I decided to plot the number of data scientist job postings from this year and last year.

The data is from Gary's Guide which does an excellent job of curating tech job postings in NYC ('Data Scientist' used for the search term). This isn't indicative of all the jobs in NYC and is quite biased given the curation but I'd imagine there would be a similar trend for all data science jobs in NYC and insightful from seasonality perspective at the minimum.

What the Data Shows

Looking at hiring trends from last year, there's two peaks: the lion's share of hiring done in the spring, a lull in late summer/early fall, and another upswing just before the holidays -- which is typical seasonality.

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Small 1545224 10152105765716192 1764874921 n Pete Soderling on

Dmitry Storcheus is an Engineer at Google Research NY, where he does scientific work on novel machine learning algorithms. Dmitry has a Masters of Science in Mathematics from the Courant Institute and despite his very young age he is already an internationally recognized scientist in his field of expertise.  He has published in a top peer-reviewed machine learning journal JMLR and spoken at an international conference NIPS. Dmitry Storcheus got peer recognition for his foundational research contribution published in his paper “Foundations of Coupled Nonlinear Dimensionality Reduction”, which has been cited by scientists and engineers. He is a full member of reputable international academic associations: Sigma Xi, New York Academy of Sciences and American Mathematical Society. This year Dmitry is also a primary chair of the NIPS workshop “Feature Extraction: Modern Questions and Challenges”.

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- Hi Dima, you were recently invited to give a talk at DataEngConf in NYC and we are very excited to hear about your novel machine learning research.  You have a pretty unique situation where you joined Google Research right after your Masters, you are a very young scientist, working together with top notch professors and Phds. Tell me about your path overall. How did you manage to get in?

- Let me first talk about my path. I studied at a Russian college called ICEF, and then I came for graduate studies to the USA, where I did my masters in math at the Courant Institute. I started machine learning research very early - back in Russia I used machine learning to forecast financial time series and in the USA I continued machine learning studies on a theoretical level. Straight after graduation I was hired by Google Research in New York to work on machine learning algorithms. I think that they key to my employment is that my unique skills and strong technical background was recognized by Google. Also, Google Research recognized my foundational scientific work that I had already done and sound machine learning algorithms that I had developed. What I did is I derived generalization bounds for coupled dimensionality reduction and created an algorithm called SKPCA.   

- So why did you choose Google over any other company?

- 2 reasons. First, a job at Google naturally followed after my research work at my graduate school as I could apply my research directly, and I wanted it to benefit the Machine Learning community. The second reason is that I think that Google is a company that values potential in people. It selects people based on their projected individual growth, and that appealed to me as a young professional because it guaranteed continued growth and mentorship by best scientists in the industry.

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Unknown author on

Earlier this year, we launched a series that profiles our hardworking iHeartRadio engineering and product team. Today, we’re excited to chat with Senior Mobile Developer Hua Wang!


Before joining iHeart, Hua worked on mobile products at Pearson, Barclays, and Apptricity, among others. With a broad range of expertise, Hua has developed mobile technologies from robots to phones. She believes that mobile is impacting our lives like never before, with incredible potential for continued future disruption.



Why did you choose to become an engineer?

Simple: I love problem-solving, and being able to solve challenging problems is critical to being a good engineer. My approach looks something like this:

-Be observant
-Stay grounded in reality with day-to-day problems
-Try to understand issues
-Address them
-Solve them

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Small 286131 Eric Bogs on

While I like to think that all of us at Hinge are perfectly aligned towards the same goals, in reality if you take a mindreading machine and eavesdrop on the internal monologues of my team members, you might overhear some... misalignment:


  • "Oh please for the love of god not another meeting, just leave me alone so I can finish building this feature" -Engineer

  • "We really need to have another meeting to sync on what's left to build this feature" -Engineering manager


and

  • "We're not moving fast enough, we need to focus more on speed so we can get this out to our users" -Product manager

  • "We're moving too fast, we need to focus more on quality, let me add some more tests" -Engineer


and

  • "This open plan space is awesome! I can talk with my team all day!" -Engineer A

  • "I can't get any work done because my team won’t stop talking about obscure movie references!" -Engineer B


You can boil each of these issues down to their emotional core: different team members’ needs cause friction and frustration. Together, these issues were compounding to a larger sense of negativity and lack of momentum. Deadlines were slipping, tempers were flaring, engineers were losing sleep and pulling their hair out, and some were resorting to elaborate dance-offs/arm wrestling/fisticuffs in the office to resolve disagreements.

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Small image Chris Angove on

I have recently been thinking about my career development path and comparing that to my father who spent 40 years working as a machine builder in the automotive sector.  Through this lens I have started to rethink how we view career development within the tech sector.

We’ve spent time and resources coming up with novel ways of organizing our companies - from lattice networks and holocracies to Spotify’s own tribe/chapter model. While specific organizational structures are important I think it neglects to take into account career development. If we are serious about building great teams we need to spend time on this area as well; instead most organizations have taken on a variation of the “white collar model” and assumes it works well.  I argue that it does not (nor do I believe it works well anywhere).

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Small ed Ed van Beinum on

Problem: You are debugging some code and you think “What on earth was I thinking when I wrote that?”
You turn to git blame* to see what the last commit message was for that line:

                Author: Edwin van Beinum<edwin@myemail.com>
               Date: Tue Aug 19 14:00:11 2014 -0400

        Various bug fixes

You find a commit message that tells you absolutely nothing.
Since this very problem bit me last week, I’ve been thinking about how to write useful commit messages. Here are six guidelines that I think will help:

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Small chris rill Chris Rill on

Does your startup need a leader, a jack-of-all-trades, or both?

When you co-found a startup, you know certain elements are crucial. A great concept that meets a pressing need. A solid founding team. An insane amount of tenacity. It’s only later you realize that creating an organization is as much an exercise in building a strong company as it is in self-awareness.

In order to fully support your startup and make it a real success, you need to understand your own capabilities, including your personal strengths and weaknesses. You must define your role, even if it’s as broad as “technical co-founder.” You have to know your high-level responsibilities at each phase of development and execute them with the same focus and intensity that more functional tasks require.

And occasionally, you have to use all that information to fire yourself.

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Small image Chris Angove on

In this talk, Spotify Chapter Lead Chris Angove discusses Spotify's culture and structure. You'll learn how Spotify's engineering team structure has changed to meet the demands of a quickly-growing startup. Chris questions the traditional career ladder and explores ways engineers and managers can create new career paths.

01:05:48

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Small 566e5c3c56d3e38a67788f56c89278e9 Dan McCormick on

Being fast and nimble is important to us at Shutterstock, and one way we accomplish this is by working in small teams. This approach has yielded tremendous benefits over the years, but it comes with its own challenges: Shutterstock now has over 300 people and dozens of teams. How do we coordinate everything with so many different groups?

Here’s a bit of information about how our approach to small teams has evolved, and how we continue to change it as we grow.

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Small steve milton Steve Milton on

"You can't build it right the first time. Don't try. Look at the problems that are going to be in front of you in the next 12 months. Pick tools and capabilities that will allow you to be successful. Live to fight another day." - Steve Milton

In this fireside chat delivered at CTO School, Steve offers meaningful advice to engineers who aspire to be CTOs at high-growth and innovative startups. Engineers who aspire to be leaders in these challenging environments have to understand how to build software in the face of uncertainty, foster collaboration between teams that are moving unbelievably fast, store and analyze increasing volumes of data, and understand how software fits in the larger challenge of building a business that people love.

Steve is currently Co-founder & CTO at PlaceIQ, one of NY's fastest growing startups. He has been building technologies and leading teams for over 20 years (you'll enjoy his stories from the "pre-Internet" days).

45:07

This talk was delivered at a CTO School meetup hosted by Pivotal in NYC.

 

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