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Deep Learning Scientist
Salary

US$175000 - US$200000 per year

Location

San Francisco, California

Description

One of the Bay area's most impactful AI Research Labs is looking to expand it's capabilities within Deep Learning to revolutionize an entire industry.

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Machine Learning Engineer - Deep Learning
Salary

US$180000 - US$200000 per year + Competitive Benefits

Location

San Francisco, California

Description

Harnham is working with a massive late-stage venture that is paving the way for machine learning. Let's talk about deep learning and/or ops research!

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Senior Software Engineer - Machine Learning
Salary

US$160000 - US$180000 per year + Competitive Benefits

Location

San Francisco, California

Description

Harnham is working with a late-stage venture that is revolutionizing how we buy products and from who, among a massive global economy - Want to join their ATG?

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Senior Deep Learning Research Scientist
Salary

US$140000 - US$160000 per year + Medical, Unlimited PTO

Location

New York

Description

Partnered with an exciting Fin-Tech company in New York City that build innovative AI solutions, looking for deep learning engineers.

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Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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Quantitative Analysts, the Science Behind the Money

Quantitative Analysts, the Science Behind the Money

Did you know New York’s Wall Street, a bastion of financial institutions, investment banks, brokerage firms and more was once an actual wall built by the Dutch to repel an English invasion? Though images of skyscrapers or movie scenes from Wall Street and The Wolf of Wall Street may flash in your mind when you think of it, the world of traditional finance has changed.FinTech businesses– a merging of finance and technology – and Challenger banks are challenging the establishment – Tier 1 banking. In an industry in which traditional banking is facing a shakeup of epic proportions from Challenger banks, finance executives increasingly turn to quantitative analysts for help. Today’s analysts want to be more invested, to make a difference and take end-to-end ownership of Model Development/Credit Strategy projects.What is a Quantitative Analyst?Quantitative analysts help financial firms make decisions about risk, pricing, and invests. But, their ultimate goal is to maximize profits – whether that be by reducing risk or generating profits – using complex mathematical models to inform business decisions.Much like the word “tech” has infiltrated other industries – advertising, marketing, retail, insurance, and so on – and the need to offer both technical (hard) and business (soft) skills remains. These analysts must be able to apply scientific methods to approach data from all angles. They must also be able to translate and interpret the information into actionable insights for their firms.Get on the Fast TrackAccording to the Bureau of Labor Statistics (BLS), the financial analyst category (inclusive of quantitative analysts) is expected to  grow 16% from 2012 to 2022 making it the fastest occupation on average. Demand is high and rising which makes competition extreme for quantitative analyst roles. Below are a few ways, you can get a leg up on the competition.Check out Michael Halls-Moore, the founder of QuantStart.com, and his Self-Study Plan for Becoming a Quantitative Analyst.#Be able to think for yourself and question everything. Look for the not-so-obvious answers.Don’t get stuck in conventional models and explore new paths. Get creative.Leave your MBA at the door. Many firms are more interested in those with a scientific background – engineering, computer science, math, or physics (natural sciences).Focus programming language studies on Python, R, and C++.Attend an event at the Wall Street Technology Association (WSTA®) created to provide opportunities to learn from and connect with other finance professionals. This year they’re launching an Innovation Showcase at its annual Summer Social on June 13. This event will showcase leading-edge technology solutions and a chance to network with other colleagues in the industry. Tickets are sold out but heads up for next year. Show Me the MoneyIf you’re a master mathematician, statistician, financier, or economist, Wall Street institutions will always need Quantitative Analysts to measure risk, to analyze, and to generate profits. After all, at nearly 30% above the national average, Wall Street is where the money is.If you’re looking for a new challenge and want get your foot in the door at a FinTech start up looking to shake up the nonprime market, we have a role for you. We’re hiring for a Lead Data Scientist to take the reins to develop, deploy, and maintain a credit-based model from scratch to enable under-served and emerging markets around the world. Contact Edward Flynn, Recruitment Consultant +1 212 796 6070 edwardflynn@harnham.comCredit Risk not your thing? No worries, check out our current vacancies or contact our East Coast team to learn more.For the East Coast team please call 212-796-6070, or email newyorkinfo@harnham.com.

Jenni Kavanagh

Jenni Kavanagh

13. June 2018

Battle Royale: Computational Biologists Vs Machine Learning Engineers

Battle Royale: Computational Biologists vs Machine Learning Engineers

From the first genome sequencing in the second revolution to Life Science Analytics as a growing field in the fourth industrial revolution, change has been both welcomed and fraught with fear. Everyone worries about robots, Artificial Intelligence, and in some cases even professionals who have stayed current by keeping up-to-date with trends. And it’s beginning to affect not only “office politics” within the tech space, but even interviewer and interviewee relationships. We’ve seen a growing trend of apprehension between Computational Biologists and Machine Learning Engineers. What could be the cause? Aren’t they each working toward a common goal? It seems the answer isn’t quite so cut and dry as we’d like it to be. Here are some thoughts on what could be driving this animosity. But first, a bit of background. So, What’s the Difference? Computational Biology and Machine Learning are two sides of the same coin; one sets the framework and the other applies what’s been learned. Both use statistical and computational methods to construct models from existing databases to create new Data. However, it is within the framework of biomedical problems as computational problems, that there seems to be a bit of a breakdown. It’s one thing to have all the information and all the Data, but its quite another to know how the Data might interact or affect the health and medications of people seeking help. This is the job of those in Life Science Analytics. Determine through Data what needs to be done, quickly, and efficiently, but at the same time, ensure the human element is still active.  A few examples of Computational Biology include concentrations, sequences, images and are used in such areas as Algorithmics, Robotics, and Machine Learning. The job of Machine Learning can help to classify spam emails, recognize human speech, and more. Here’s a good place to start if you’d like to take a deeper dive into the differences between the two or read this article about mindsets and misconceptions. Office Politics in the Tech Space Circling back to the concern between Computational Biologists and Data Scientists with a focus on Machine Learning. The latest around the water cooler within the tech space is that those with a biological background who understand Machine Learning are looked upon as dangerous to the status quo.  But, as many of our candidates know, it’s important to stay on the cutting edge and if that means, upskilling in Machine Learning so you have both the human element as well as the mathematical, robotic components, then that is more marketable than just having one or the other. The learning curve in biology training within the Life Sciences Analytics space means Computational Biologist with a Machine Learning skillset is best able to apply Data Science and computer science tools to more organic and biological datasets. Someone with just a computer science background may not have the depth of knowledge to understand how these models, systems, and data affect and impact medicine. Computational Biologists who are trained simultaneously in computer science and biology, and are a little heavier on the biology side, see Machine Learning Engineers as a threat because utilizing Machine Learning and other cutting-edge tools could mean their job is on the line. They worry their job will fall by the wayside. That when somebody proves Machine Learning is faster and more efficient the impetus might be why hire a Computational Biologist when a Machine Learning engineer will do? It’s like when a lot of people joke about how robots are going to take over the world and everybody will be out of a job. I think the worry with some folks on the Computational Biology side is that maybe they just aren’t up to date with their training or haven’t kept up with cutting edge of technology. With a Recruiter’s Eye While what I’ve seen agrees that, yes, Machine Learning is incredibly helpful and fast and you can get through so much more data. But its still that understanding of biology and chemistry that you will need because you need to be able to understand, for example, how these proteins are going to be reacting with one another or you need to understand how DNA and R&A work, how best to analyze, and what analyzing those things means. On the other hand, just because you know, “oh, this reaction comes out of it”, if you don’t know why that is or how that could impact a drug or a person, then you don’t really have anything to go on. There’s a caveat there. Though there may be concerns among Computational Biologists and Machine Learning Engineers, at both the upper and entry levels, it’s still the technical lead who will say, “we really do need somebody with a biological background because if we get all this Data and don’t really know what to do with it, then we’ll need to hire a Project Manager to converse between the two and that’s an inefficient use of time and resources”. What I hear most often is a company wants a Computational Biologist but they also want someone who knows Machine Learning. But they don’t want to compromise on either because they don’t understand there are limitations to things. We all want the unicorn employee, but we can’t make them fit into a box with too specific parameters. It’s a Fact of Life Any job, whether it’s in the tech industry, the food industry, Ad Optimization, or even recruitment, uses Machine Learning in one way or another. Yet compared to spaces which work on sequencing the human genome, it's amazing to see how far things have come. It used to take days to process DNA. Now you can spit in a tube and send it off to 23andMe to learn a little about your health. That’s what Machine Learning enables people to do. But it doesn’t mean Computational Biologists are going to fall by the wayside. It means there will be times you’ll have to liaise more between the two groups. It means you’ll be more marketable by adding Machine Learning to the work you’re already doing or taking some classes in Computational Science, for example, to keep your skills up to date. It’s a Transparency Issue Ultimately, it seems the heart of this apprehension comes down to a transparency issue. For example, let’s say companies begin to bring in AI people and suddenly the staff already in place begins to get worried about the security of their jobs. Even in an industry tense with skills gaps, the fear still abounds. In coming back to speak with the Hiring Manager, it became clear the animosity is even more prevalent than first imagined. So, it’s important to get input from within the company and develop a unified story, a unified message across departments, and especially within the Life Science Analytics and Data Science teams as well. In other words, “keep people in the loop.” If it’s happening to this company, it seems other companies may be facing this same issue. However, it’s not going away and is creating a kind of competition between the old guard and the up-and-coming startups. For example, any new company is going to want to integrate AI and will be asking the question how best to integrate it into their structure. They might also ask how best to optimize the ads coming through AI. This is just another way of how companies are catching up, but also how people are catching up to the companies.  Technology is coming whether you like it or not. So, if you want to stay marketable and work on really interesting projects, there’s always going to be the challenge of staying up-to-date and different companies attack this in different ways.  Stay open minded, keep an eye and an ear out for ways to stay on top of your game. Even just taking a few minutes to watch a YouTube video, listen to a TedTalk or a podcast, so you can talk about it and be informed. These are some really simple ways to stay on the cutting edge and help you figure out where you can grow and improve for better opportunities. Ready for the next step? Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Alyssa Liew

Alyssa Liew

04. July 2019

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