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Tim Jonas

Senior Consultant - US East

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Data Scientist - NLP

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US$160000 - US$180000 per year + Bonus + Equity

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New York

Description

Harnham are partnered exclusively with one of the fastest growing technology startups looking for a commercially experienced technical lead for their NLP team.

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54676 VACTJ

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Principal Data Scientist

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US$200000 - US$210000 per year + Bonus + Benefits

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New York

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You'll be an integral part of this new team's growth, leading all things data science and working closely with real estate experts.

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Senior Statistician

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US$150000 - US$170000 per year + Equity + Benefits

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New York

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Be part of a high caliber team propelling the medical imaging space through Data Science!

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Director of Data Science

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US$240000 - US$260000 per year + Bonus + Benefits

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New York

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Are you looking to work with cutting-edge technologies in natural language to disrupt the financial space? One of the leaders in finance is doing just that!

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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.

Visit our Blogs & News portal or check out our recent posts below.

How Cities Are Using Big Data

High speed trains in Florida. Driverless cars in Arizona. National grid union agreements. All these and more are working to create a more smoothly operating system of infrastructure. While privacy laws and transparency vie for attention at every level of government in the US, cities have taken the onus of using data to make decisions. The functionality a critical infrastructure society is built on – railroad tracks, flare stacks, power lines – has been brought together by robotics and AI. The decentralization of intelligence, cloud systems which remotely control Industrial IoT, and AI are just a few of the ways in which 2019 will be a breakout year for Distributed AI. New York City Uses Data to Alleviate Damage Risk to Buildings In their race to stay ahead of Big Data, they may also find ways to improve they might never have discovered without it. New York City has limited staff who can analyze its million properties and incorporate analytics to discern fire risk considering past risk and building traits. City coding has therefore become more important than ever to alleviate potential risk. Philadelphia Focuses on City Interaction with its Residents Evidence-based decision making has debuted in Philadelphia’s GovLabPHL, a multi-agency collaboration. Together, they are centralizing and digitizing records making information easier to share among agencies that historically kept information to themselves. With everything in one place, they can provide city services to their residents much more effectively and efficiently. Florida’s First High Speed TGV Train Rolled out late last year, this high-speed train travels from Miami to West Palm Beach with plans to branch into Orlando and Tampa soon. America’s first high speed passenger train in years will help alleviate road traffic, noise pollution, and more. Data collected may include best safety measures, business practices, and economic value to the city and its residents as money shifts from car buying to rail ticket purchases. The Ethics of Data and Potential Risk of Bias Gaining insights into human behaviors, ease of transportation, and predictive information to curb damage to buildings and other city properties are all important to a smart city’s infrastructure. But, data is, after all, input by humans and isn’t infallible; falling prey to natural biases. Researches and analysts caution decision-making from computer-based algorithms isn’t perfect and should be considered with discretion. For example, the rise in AI, face recognition software, traffic cams, and statistics currently on file may hold a prejudice against certain ethnicities based upon their developer’s biases. This is especially glaring in criminal behavior predictions and as such, policymakers need to think critically and to not take technology at face value. After all, those inputting the data are human, and our biases have a way of seeping into our information. In 2019, AI systems are no longer the robotic machines once shown in movies as something to fear. Today, vendors who build these systems must not only focus on the value provided, but also consider moral foundation of their service. It’s important to understand exactly why and how data will be collected and with whom it will be shared. As cities and businesses continue to catch up, this knowledge is necessary for long-term viability, credibility, and transparency. Trust is a crucial element of data strategy. See Through Cities – Transparency is Key City governments and researchers are working to lessen discriminatory outcomes by turning to transparency. Major cities such as Philadelphia and New York have opened up their websites and invited the public to examine information and their methods of interpreting the data. New York implemented a task force to study how the city uses data and its goal is to present in December of this year ways the city should assess its automated decision-making for transparency, equity, and opportunity. This is a pivotal year for cities to understand their urban ecosystems. Understanding challenges such as traffic, pollution, parking and inefficiency of movement in urban areas may help realize how, when, and where people are moving. With core infrastructures in place, movement may be reduced. In addition, mobility will become efficient and lessen people’s need to move around for better jobs and/or housing. AI is the tool to help cities gain visibility into this type of data. It will enable not only visibility, but also foster prediction capabilities, and provide actionable insights to improve our understanding of why, how, and the way we move. If you’re interested in Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. 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.

by Tim Jonas

09. January 2019

HOW AI CAN SUPPORT WORKERS IN THE WORKPLACE OF THE FUTURE

This Monday was Labor Day; a tribute to the American worker. Established over a century ago as the Industrial Age got under way, the holiday was meant to showcase their strength, freedom, and leadership. Now, as the Fourth Industrial Revolution ushers in a new age of robotics, automation, and digitization many workers may wonder how they’ll fit into the new workplace. These concerns are being addressed in a land known for its startup and innovation culture. San Francisco. For 3 days, the city will host an Artificial Intelligence Conference focused on how to use emerging AI techniques in real-world implementations. Will Robots Take My Job? Movies and books tend to offer cautionary tales of problems which might occur if rapid changes go unchecked. It’s unlikely, however, that you’ll find yourself fending off a group of Androids or Terminators any time soon. AI, like all forms of progress, is intended to help improve the quality of human life. Imagine, for a moment, life before washing machines, dishwashers, microwaves, and mobile phones. Not only were these items intended to improve lives, but to give people more time for other things like creativity and cognitive thinking. At the AI Conference in San Francisco, Adam Cutler of IBM Design is taking a deeper dive into how human and machine can form meaningful relationships. It is one thing to respond to machines like Alexa or Siri, who are boxes with voices, but what about when there are more Sophias in the world? Sophia is the first ‘robot citizen’ created by Hanson Robotics. She looks, talks, and reasons much like we do. Though repetitive jobs such as manufacturing may be more affected as robots will be able to make things for longer, at a faster pace, there will still need to be oversight. Even here, there will still be a need for humans to make higher level decisions. Humans are able to gauge reactions whereas robots will look only at the data. How Businesses Can Integrate AI into their Enterprise In the world of startup cultures, AI may be seamlessly integrated and built into the culture or around it, but what about legacy companies? Those who have been following the same patterns for 25, 50, or 100 years? In Taming dragons: a breakthrough approach to AI for business leaders, the discussion focuses on how to implement enterprise AI into businesses. Every new technique we introduce is nuanced and massaged to make the pill of change easier to swallow. But, if we grab for every shiny new toy without fully understanding why we need it and how it can help us improve our bottom line, we risk either misusing it or tossing it aside because it didn’t deliver as promised. What Can I do to Futureproof My Job and Myself? Ultimately, the key is to always be learning. Understand the deeper changes and effects of AI in the marketplace and figure out where else you might be able to use your skills. Diversify. Get creative. Determine your strengths, weaknesses, opportunities, and threats. See if you can’t spin those threats into opportunities. Technical skills are high priority, of course; computer science, Data Science, and Natural Language Processing are all woven into the tapestry of today’s job market. But, as AI capabilities continue to grow, it’s skills such as communication, creativity, empathy, and leadership where humans will continue to have the edge. Want to learn more and see if we have a role for you? Check out our current vacancies. See where Harnham can take you. For our West Coast team, call (415) 614 4999 or email us at sanfraninfo@harnham.com. For our Mid-West and East Coast teams, call (212) 796 6070 or email us at newyorkinfo@harnham.com.

by Tim Jonas

04. September 2018

Finders Keepers: how employers hire and retain top data science talent

Data drives business and Data Scientists are in high demand. Employers know hiring the right talent is the key to a healthy bottom line now and in the future. As the year comes to a close, many are hurriedly finalizing their fourth quarter numbers, while others think ahead to January’s New Year resolutions. In the first of our 2-part series, we’ll focus first on how and where employers find and retain talent. Next, we’ll focus on how employees can improve their chances to land one of the hottest jobs for 2018.In our 2017 salary guide, we can help you find the best talent at the right price. According to our latest big data conference 76 percent of employers are seeking to grow their analytics team; learn how to find and keep top level data and analytics employees for your business.Where the Data Scientists areWhile every state has some concentration of data science professionals, the hotspots for deep analytic talent are found in large metro areas such as New York, New Jersey, Boston, Washington D.C., Chicago, and San Francisco. Of the five largest metropolitan areas, San Francisco tops the list for deep analytic talent. As tech companies move their Silicon Valley startups from San Jose to San Francisco, employers are upping their game to find and retain big data professionals. An estimated 65 percent of San Francisco startups are less than eight years old offering the perfect opportunity to grow a data science team from the ground up. Beyond the unicorn - What employers look for in a data scientistCEOs are looking for problem-solvers in technology-rich environments. Problems are like people, everyone is different and with no unifying common language to identify competencies in job postings, attracting candidates needs to be approached differently.Rather than look for unicorns, that one mythical creature which encompasses it all, employers are building data science teams. Data science is more than simply looking at numbers and determining algorithms, it requires creativity and non-linear thinking; it’s looking at numbers and applying it to people. The more diverse skills and backgrounds, the greater insight can be brought to analytics. One key skillset is for data professionals to be able to tell a story with data.Competencies needed for data analytics roles often include strong credentials in both education and experience. The nature of data science requires a high bar of educational experience with employers typically interested in a college degree and three to five years’ experience with about a third of job postings requiring an M.A. or higher. But, just as data scientists may be tasked to think outside their comfort zone, so too must employers. Demand is often much higher than supply, so employers must think more creatively and strategically about finding talent. Many businesses often look inward to identify staff with potential to learn new skills. Best Practices for Retaining TalentWhether employers begin in-house or seek outside candidates the goal is to ultimately keep such talent. Integrating data science across the company and keeping data scientists engaged in meaningful work are a few ways to retain top talent. Good data scientists are in high demand and competition is fierce. To keep them motivated and excited to stay, best practices include offering support, a sense of ownership, and a strong purpose. Support can be as simple as making sure your data science team has the tools they need to succeed such as the correct software to an investment in education. Some companies may even offer opportunities to meet with other academics and data scientists from other companies to build a team’s skillset with new ways of doing things and helping them to keep with the trends in the sector. Data scientists don’t work in a vacuum. Recognition and a sense of ownership work hand-in-hand to ensure a deeper understanding of the problem and opportunity to present their analyses to the decision makers. Ultimately, motivation lies in the opportunity to solve challenging problems and by engaging their minds with interesting data sets and questions to solve keeps them mentally sharp and more engaged with the company.Companies everywhere need data talent to help them grow and improve their bottom line, but companies with more complex problems and continual data flow have the best chance of attracting deep analytic talent.If you’re interested in building your data and analytics team, we may have the data professional for you. We specialize in Data and Analytics recruitment and always have a wide range of Data Science vacancies at both junior and senior levels.For the East Coast and Mid-West teams please call 212-796-6070, or email newyorkinfo@harnham.com.For the West Coast team call 415-614-4999 or email sanfraninfo@harnham.com.

by Tim Jonas

21. November 2017

How to become a data scientist

Looking to ramp up your career prospects in 2018? Then consider data science. As a Data Scientist, you’ll be filling a much needed role for businesses industry-wide and will be in what a recent article called “this century’s hottest career”. Last week, in the first of our 2-part series, we talked about how employers find and retain top data science talent. In this article, we’ll focus on how prospective employees can improve their chances in the market.As the year comes to an end, revenue growth is expected to double for a third of Fortune 500 companies. But, it’s not just the big boys who’ll need Data Scientists and be growing data science teams. More often than not, you’ll be more likely to get your foot in the door through one of the millions of startups and small to medium businesses. In San Francisco, one of the hiring hotspots for data professionals, 65% of businesses are startups less than eight years old. Basic Skills and ToolsWhile degrees both undergrad and higher education abound, close inspection of job descriptions requires at the least a bachelor’s degree for aspiring and junior level professionals. Advanced positions may require advanced degrees, though the basics and experience count a bit higher. Application of theory is key, so knowing the basic skills help to set up a solid foundation on which to build a career.Python, R, and SQL; nope, it’s not an eye chart. These are a few of the basic statistical programming language you’ll need to know. In addition to these languages, a knowledge and understanding of basic statistics; while useful in machine learning applications, it can also help define, design, and evaluate experiments for decision makers. And last, but not least, a solid grasp of linear algebra and multivariable calculus, round out the basics of a data science skills toolbox. But, what to do with all that data; how do you translate it to explain customer behaviors and how the customer experience can be improved upon? A data scientist who can crunch the numbers, then tell a story is the penultimate employee for any business. It’s this disparate combination which has led to the creation of data science teams. Tell Me a StoryKnowing complex math, statistics, algorithms, and computer languages are basic components of data science, but how do you explain your findings to the decision makers. Companies need data-driven problem solvers, but translating analysis requires a different type of thinking. Think beyond the numbers and consider what is important to the company to help them test an idea or develop a new product. What isn’t important? How should you interact with your team? What about those outside your team such as engineers or product managers? Knowing the kinds of questions to ask and understanding the business can help guide how information is communicated. The ability to communicate for both technical and non-technical audiences is the next step in the process to improve your chances in the market. Data Scientists who can describe their findings and how various techniques work; help decision makers visualize the data as well as the principles behind the information. Looking AheadIn 2011, the McKinsey Global Institute (MGI) report estimated a shortfall of data scientists in the U.S. of between 140,000 and 190,000 by 2018. As we approach 2018, it is now estimated millions of people will be needed to translate data scientist’s work to the organization. Though it’s been suggested that domain experts obtain data analysis knowledge to act as translators, a data scientist with a deep knowledge of their company and its business, who can translate their findings, will be a boon to their organization.If you’ve set a new year’s resolution goal to raise the bar on your career and are interested in big data, check out our current vacancies here. Harnham specialize in Data and Analytics recruitment and have opportunities at both junior and senior levels. For our West Coast team call 415-614-4999 or email sanfraninfo@harnham.comFor the East Coast and Mid-West teams please call 212-796-6070, or email newyorkinfo@harnham.com.

by Tim Jonas

11. November 2017

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