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Data Science Jobs in New York

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Lead Data Engineer (Machine Learning)
Salary

US$190000 - US$200000 per annum

Location

New York

Description

One of the most successful video game companies in the world is looking for an experienced data engineer to lead a growing team

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Data Scientist - (TS/SCI Polygraph Required)
Salary

US$140000 - US$150000 per annum

Location

Boston, Massachusetts

Description

One of the most innovative and well-established companies in the world is looking for a data scientist with secret clearance to work on exciting AI projects

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Chief Data Scientist
Salary

US$225000 - US$250000 per annum + Bonus + Equity

Location

Boston, Massachusetts

Description

This is a player/coach role where you'll play an integral part of the team development and data science strategy with the executive team.

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Senior Data Scientist - NLP
Salary

US$145000 - US$165000 per annum + Equity

Location

Boston, Massachusetts

Description

This is an opportunity for a well-rounded data scientist to use their knowledge of Natural Language Processing (NLP) and Machine Learning for real time use!

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Associate Director of Data Science
Salary

US$160000 - US$180000 per annum + Bonus + Benefits

Location

Boston, Massachusetts

Description

This is a technical leadership position where you'll work closely with sales and marketing teams on impactful data science projects.

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Associate Manager, Data Science
Salary

Negotiable

Location

New York

Description

Are you passionate about healthcare? Join a leading life science company with a focus in the healthcare field looking to improve the world's quality of life.

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Senior Machine Learning Engineer
Salary

US$160000 - US$180000 per annum

Location

Boston, Massachusetts

Description

This is an opportunity for an innovate machine learning engineer to use their expertise in NLP to identify trends around the world.

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Deep Learning Engineer
Salary

US$120000 - US$150000 per annum

Location

New York

Description

This is an opportunity for a pure Deep Learning Engineer to design unique infrastructure to change the world of online shopping

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PRINCIPAL ANALYTICS CONSULTANT
Salary

US$20000 - US$220000 per annum + bonus, benefits, unlimited vacation

Location

New York

Description

Looking for a principal analytics consultant to come innovate and strategize the way this legendary healthcare company enters the retail space.

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Director of Machine Learning
Salary

US$210000 - US$240000 per annum + Equity

Location

Boston, Massachusetts

Description

This is an opportunity for a Leader in Machine Learning with a specialization in geospatial data to take their talents to a technology company.

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Senior Machine Learning Engineer
Salary

US$155000 - US$175000 per annum + Equity

Location

New York

Description

This is an opportunity for an innovative machine-learning engineer to use their expertise in NLP to find trends around the world and identify correlations.

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Senior NLP Engineer
Salary

US$160000 - US$180000 per annum

Location

New York

Description

This is an opportunity for an innovate machine learning engineer to use their expertise in NLP to identify trends around the world.

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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.

Four Ways Advanced Analytics Drives Business Forward

2020 was an unprecedented year for shifting businesses online. Technology, never quite in the background, arrived center stage to help drive transformation in a variety of industries. Many businesses were forced to change their processes, how they interacted with their employees, customers, and with each other. One of these major shifts was in Advanced Analytics and Insight. Stemming from a Marketing perspective which had specific deliverables of demographics, location, and consumer histories, advancements found a place in working with unstructured Data. Working in tandem with these new analytical insights, artificial intelligence brought learning, problem-solving, planning, and other naturally human behaviors to life. This includes in the creative fields, not just in traditional industries like Finance or Retail.  In a study conducted by Forrester on behalf of Intel, though most businesses know Analytics can drive their business forward, less than half are taking advantage of these transformational technologies. Below are a few ways Advanced Analytics can drive business forward. 1. Decision Science and the CDO Roles Will Grow In a seemingly counterintuitive measure, while most businesses were cutting back in IT, Data and Analytics budgets were expanded. As the Chief Data Officer and Decision Science roles increase in importance, businesses who know the value of their Data can derive actionable insights and business decisions from these executive level communicators. 2. Access to a variety of Data Sources Will Help to Streamline Business Operations With most businesses operating strictly online or in a hybrid ecosystem, optimization of processes is key. In the ever-changing market systems, buyer behaviors and the consumer journey will increase dependency on Data and Analytics as businesses seek to meet consumer demand. Offering bespoke solutions and coordinating such Data sources as chatbots and call centers, businesses will have the opportunity to create a seamless system as they adopt and implement technologies such as Advanced Analytics and AI. In the right mindset, these practices can also drive partnerships within their ecosystems from Data Science to technology vendors with AI capabilities.  3. Sharpening Focus on Measurable Projects to Increase ROI Rather than rely on third parties, Data will become part of the business offering value in their operations. It will drive how they operate, deliver, and understand the needs of their consumer. Owning and managing their own Data will provide unique insights they may not have been aware of before. Sharpening their focus to get a good return on their analytics investment, businesses will broaden their ecosystem. Seeing the bigger picture, businesses will also want to access more specific insights that drive actionable answers to their questions. 4. Machine Learning, NLP, and Domain Expertise Can Help Scale Data Modelling As AI, Advanced Analytics, NLP, and Machine Learning platforms come into full swing and in combination, new Data Modelling opportunities can increase insight. Automated processes of Data classifications will drive scale increasing both the amount of Data and a granular level of detail to be extracted.  The specialization of these Data platforms will only grow in importance. In our always-on, always on demand world, the need for Advanced Analytics professionals and a variety of posts in the Data profession, businesses will expect strong domain knowledge. They’ll be looking for professionals and platforms which can help them understand specific use cases. Rather than just simple demographics and birds-eye views of their consumers, they’ll want to drill down to not only what they can provide now in terms of goods and services, but anticipate what consumers will want and need for the future. In the last year, we’ve absorbed a lot of information, and have struggled to distill it in actionable insights. But, if you’re interested in Marketing and Insight, and would like to shift into Advanced Analytics and Insight, we may have a role for you. Not your bag, but interested in Life Sciences, Decision Science, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out 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.  

Charles Austin

Charles Austin

21. January 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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