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Data Science Jobs in New York

NEW YORK DATA SCIENCE JOBS

We help the best talent in New York, find rewarding careers in Data Science. 

Data drives business and, in the 21st century, the Data Scientist is the “rock star” of the technology world.  Companies today know insights are the way to a higher ROI, a healthier bottom line, and, ultimately, a loyal client and customer base. To stay ahead of the competition, companies must continuously look for new and innovative ways to extract insights from the large volumes of data they acquire. 

Our aptitude in matching the best talent with the best companies is second to none. Our clients range from FTSE 100 organisations to small start-ups looking to shake-up the status quo. If you’re looking for your next challenge in the Data Science industry then get in touch and take the first step on that journey.

STAFFING & RECRUITMENT SOLUTIONS

Talk to Harnham about how we can help your team or organisation find the best Data & Analytics talent!

Our recruitment solutions are determined by tried and tested methods to ensure an effective solution for all concerned. Our focus on establishing networks of skilled individuals, as well as knowing our client businesses, culture and skill requirements guarantees we have a high success rate on our placements. 
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Latest Jobs

Principal Data Scientist
Salary

US$130000 - US$160000 per annum

Location

New York

Description

Are you an experienced Data Scientist with extensive experience in marketing measurement and attribution? Then this is the role for you!

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NLP Data Scientist
Salary

US$150000 - US$170000 per annum

Location

New York

Description

This is an opportunity for an innovate data scientist to use their expertise in NLP to work on innovative projects in the health-tech space

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Data Scientist - Digital Marketing
Salary

US$100000 - US$120000 per annum

Location

New York

Description

Looking for your next role with a data-driven branding agency? We have a passionate and creative agency looking for their next Data Scientist.

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Principal Data Scientist
Salary

US$130000 - US$160000 per annum

Location

New York

Description

Are you an experienced Data Scientist solving real-world problems that have significant commercial Impact? Then this is the role for you!

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Principal Data Scientist - Marketing Measurement & Attribution
Salary

US$130000 - US$160000 per annum

Location

New York

Description

Are you an experienced Data Scientist solving real-world problems that have significant commercial Impact? If so, then this role is for you!

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Principal Data Scientist, Experimentation
Salary

US$130000 - US$160000 per annum

Location

New York

Description

Are you an experienced Data Scientist solving real-world problems that have significant commercial Impact? If so, then this role is for you!

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Director of Data Science
Salary

US$200000 - US$250000 per annum

Location

Boston, Massachusetts

Description

An ecommerce powerhouse worth over $4bn is looking for a Director of Data Science to lead a data science and econometrics team

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MACHINE LEARNING ENGINEER, BOT MITIGATION
Salary

US$880 - US$1600 per day + benefits

Location

New York

Description

Looking for a lead machine learning engineer who can lead a bot mitigation project working with stream data for a global sportswear leader.

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Data Scientist -- Video Game Start-Up-- Remote
Salary

US$130000 - US$150000 per annum

Location

New York

Description

A technology-based remote video game start-up with significant funding is looking for its first data scientist skilled in machine learning.

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Statistician -- Health Tech Company
Salary

US$140000 - US$170000 per annum

Location

New York

Description

An innovative health-tech company is looking for a statistician to aid them in their mission to revolutionize patient care utilizing AI & advanced analytics.

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Principal Data Scientist - Experimentation
Salary

US$130000 - US$160000 per annum

Location

New York

Description

Are you an experienced Data Scientist solving real-world problems that have significant commercial Impact? If so, then this role is for you!

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Senior Data Scientist
Salary

US$140000 - US$150000 per annum + Bonus + Benefits

Location

New York

Description

This is a senior-level IC role where you'll be responsible for using your domain and technical expertise on breakthroughs in meaningful disease.

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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.

Making Sense of Unstructured Data with NLP

Natural Language Processing. It seems a simple enough explanation. The idea is to make computers sound like native speaking humans regardless of their language. Except there’s one problem. When we speak, we don’t follow our own rules of grammar. We use idioms, metaphors, abbreviations, and oftentimes use more body language to communicate than we realize.  So, what’s a poor machine to do when confronted with such an unstructured melee of data? Well, since semantics is not what you say it’s how you say it, we must teach computers to read between the lines. Of code. Enter NLP. The semantics of human language written for a machine to help make sense of our human behaviors gets organized. The Perfect Imperfections of Language Computers require structure. Natural language does not. Teaching machines how we communicate is no easy task, and yet we use machines that can do this every day. By combining technology and Machine Learning we begin to teach computers how to understand us. We teach them how to interpret and determine what it was we want done. When you’re asking Siri or Alexa a question, you’re helping them to learn how you ask, so they can better respond, and they make you more efficient. It’s a win-win for everyone. In business, using NLP techniques to drive business decisions is even more important. Now, the computer must decide what information is the most valuable to pull from a pile of Data. Understanding our choices, our tone, even the words we choose to use, helps our machines learn what we want to do or need done. Where is NLP Used? Since we use different rules when we speak than when we write, our computers learn how we talk and how to use language more naturally. Wondering where NLP might be used? In a word or two? Nearly everywhere. You are scheduling a meeting and when it’s time, a calendar reminder pops into your phone which says estimated drive time to the meeting based on traffic conditions in your area. Or you ask Alexa to play your favorite music list from Pandora.  Every touchpoint in this scenario is using NLP. We naturally might get into our car, ask our Virtual Assistant navigation system for directions, or to play our favorite music. Our choices don’t fit in a box and may not be logical, but the more we teach the machines, the closer they may get to understanding the nuances of our language. Here are 5 more ways we use NLP every day: Predictive text on your phone or in your Word document. Chatbots and Virtual Assistants to ensure customers are acknowledged in a timely manner, answer basic questions or redirect to appropriate personnel, and making suggestions to improve the customer experience.Curating social media feeds to determine customer needs and interest.Grammar correction software so our emails and business documents are error-free.Analyzing customer interactions using comments and reviews for customer feedback about a product or service. There’s a ton of information to be filtered, sorted, sifted, and analyzed, and NLP is just one of the tools Data Scientists use. Interested in the subfield of NLP? Check out this article for 6 techniques you need to know to get started. Already well-versed in the industry and looking for a new challenge? If you’re interested in Big Data and Analytics, Advanced Analytics, Life Sciences, Data Science, or any of our Data professional fields, we may have a role for you. Review our current vacancies or contact one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Elizabeth Sobel

Elizabeth Sobel

25. March 2021

Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

William Reichert

William Reichert

19. March 2020

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