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Salary

US$165000 - US$185000 per year + Competitive Benefits

Location

San Francisco, California

Description

Harnham is working with a global e-commerce marketplace that's changing how goods are delivered to consumers - It's your chance to automate their marketplace!

Salary

US$115000 - US$130000 per year

Location

Boston, Massachusetts

Description

Privately funded company is transforming the discovery of natural resources.Seeking Data Scientists experienced with ArcGIS and machine learning.

Salary

US$270000 - US$320000 per year + Competitive Benefits

Location

San Francisco, California

Description

Harnham is partnered with a leading Ad-Tech company to direct the roadmap for a robust team of data scientists and machine learning engineers.

Salary

US$160000 - US$180000 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. If you know all about deep learning - Let's talk!

Salary

US$140000 - US$180000 per year

Location

Santa Clara, California

Description

Harnham is working with an industry leading social analytics platform and this is your chance to join one of the most talented teams in the world!

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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.

MACHINE LEARNING ENTERS BIOINFORMATICS AND ITS FUTURE IS BRIGHT

Machine Learning Enters Bioinformatics and its Future is Bright

Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help.  So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings.  Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example.  The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work.  Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper.  Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close.  Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our 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.

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