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Data Science Jobs in New York

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Head of Machine Learning
Salary

US$240000 - US$260000 per annum + Amazing Benefits

Location

New York

Description

This is an opportunity for a Machine Learning Engineer to take their talents to a food delivery tech firm who are continuing to grow despite the climate.

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SENIOR Data Scientist ( Causal Inference)
Salary

US$150000 - US$170000 per annum

Location

New York

Description

A healthcare start-up focusing on changing the effectiveness and quality of care is looking for a data scientist with a rare skill set in causal inference!

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Senior Data Scientist - Machine Learning
Salary

US$135000 - US$150000 per annum + Amazing Benefits

Location

New York

Description

As a Senior Data Scientist you will work with some of the best Data Scientists and Engineers in New York in the world's most innovative medical-tech startups.

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Principal Data Scientist
Salary

US$200000 - US$250000 per annum

Location

New York

Description

This is an opportunity for an experienced Data Scientist to come into a well-established start-up and build their AI infrastructure from the ground up

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Team Lead Machine Learning Engineer
Salary

US$240000 - US$260000 per annum + Amazing Benefits

Location

New York

Description

Technical Team Lead -This is an opportunity for a Machine Learning Engineer to be technical lead for a food delivery company who are growing amidst the climate

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Data Scientist - Machine Learning
Salary

US$135000 - US$155000 per annum + Amazing Benefits

Location

New York

Description

As a data scientist you'll work at a company that applies advanced machine learning models to all types of medical imaging and has had unprecedented success.

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Machine Learning Engineer - NLP
Salary

US$130000 - US$150000 per annum + Bonus + Benefits

Location

Boston, Massachusetts

Description

This is an opportunity to work with a highly skilled machine learning team in the healthcare space where you'll work extensively on natural language projects.

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Engineering Manager
Salary

US$250000 - US$275000 per annum + Equity + Benefits

Location

New York

Description

This is a unique opportunity to build out a brand new ML engineering team with a Series C startup.

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Engineering Manager - ML
Salary

US$225000 - US$270000 per annum + Amazing Benefits

Location

New York

Description

his is an opportunity for a Engineering Manager in Machine Learning Engineer with one of the top players in social media.

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Lead Data Scientist
Salary

US$170000 - US$190000 per annum + Bonus + Benefits

Location

New York

Description

This is an exciting opportunity to be the technical lead and healthcare domain expert for a blossoming data science startup.

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Principal Data Scientist - Remote
Salary

US$170000 - US$190000 per annum + relocation, benefits, bonus

Location

New York

Description

We are working directly with a team providing their clients DaaS and also building into platforms as SaaS, aiming to advance AI abilities.

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Causal Inference Specialist (Data Science)
Salary

US$160000 - US$185000 per annum + Great Work Life Balance

Location

New York

Description

This is an opportunity for a top of the line Causal Inference specialist with a Ph.D. to work in a cutting edge field of medical technology.

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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.

The Transformative Nature of Data Science

What does Data tell us? Why do we want to know the information we collect and analyze? How can Data help us now? Well, sometimes you have to go back to basics. Knowing your Data helps you make better decisions for the future. This is the transformative nature of Data Science.  The Data Scientists have collected is leading toward a vaccine for a novel virus. Our massive shift from in-house workers to working from home showed us where the gaps in our internet infrastructure existed. But, the information we gathered wasn’t just where the gaps occurred, it also offers a jumping off point for how we can use what we’ve learned to improve.  We know Data is essential to business today, but how we use it, and gathering what we can learn from it, offer transformative advantages we might have otherwise missed. WHERE DO WE GO FROM HERE? Data Science is what helps us interpret the massive amounts of Data we’ve collected. With an estimated 90 percent of Data created just in the last couple of years, and an estimated dearth of connected devices estimated to grow over 75 billion by 2025, the sheer volume is daunting. Yet we still have need for change. Technologies to interpret Data at such a massive scale still need someone to gather, collect, analyze, and interpret the information. What we’ve learned so far with Data Science shows us what must change to support health workers, the health of our employees, the support of remote workers and gig workers, and how businesses can differentiate themselves from their competitors in a post pandemic world. SEVEN WAYS ORGANIZATIONS CAN PREPARE FOR THE FUTURE Prioritize Digital Collaboration As employees begin to return to ‘normal’ work hours, Gartner suggests 48% of employees will work remotely. This is an 18% increase pre-pandemic. So, when hiring managers take stock of their employees, they’ll want to consider things such as productivity and performance management and how workers are evaluated based remote working touchpoints rather than established criteria of employee performance management.Ensure Inclusivity of Employees  Bring employees into more critical roles and give them the freedom to make mission critical decisions. Open Up Opportunities and Develop Critical Skillsets Coach employees on how to develop critical skills for a variety of roles, rather than focusing on one particular role.Be Flexible The days of doing things ‘the way we we’ve always done’ are gone. It’s time to reassess, reevaluate, and prepare your employees for success. How? Consider what may may be needed for a given role’s development path. Do your employees need reskilling or upskilling?  Flexible careers. The gig economy. The freelance economy. Contractors. All these titles and labels offer flexible learning and training allowing your business to pivot smoothly and efficiently as needed. Training is the key here and it will help employees transition into other organizations, into roles with greater responsibility, and allow both your employees and your organization to adapt to changes more quickly.Teach Employees to Respond Rather than React Structure your organization and employee response to quickly course correct. Don’t assume or target a core set of future skills. We don’t know what the future holds or what skills may be needed. But if you have employees with wide interests, your business may be better positioned to make changes as needed. Implement a Culture of Inclusiveness – Remote vs. In-House Employees Diversity is an important part of any business. But with the rise of remote workers, it’s time to ensure all workers are supported in regard to healthcare coverage, mental support, and financial health pre-and post-pandemic. Inclusiveness can help to engage those workers both in-house and remote to ensure everyone feels part of the team. Devices such as VR, AR, video calls, and more can help to make every employee feel part of the company culture.Encourage Data Literacy Throughout Your Organization Everyone in your organization will need to be Data literate. Yet everyone will be at a different level of literacy. Here it’s important to define both the skills and capability. Once your leadership has a firm grasp of the Data provided to them by your Data Scientists and business intelligence analysts, then they’ll have a starting point from which to make informed decisions. Building a culture of Data begins with leadership. Are you ready for the role or are you an organization looking for someone to fill this role? Business processes have shifted online, looking for your next job has become more daunting than ever before. But here’s the good news. Everyone’s on the same page. Leaders, hiring managers, recruiters, and prospective employees are all navigating a new way of doing business and finding talent to keep those businesses running.  If you’re interested in Big Data and Analytics or other Data professional opportunities, 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.  

Kristianna Chung

Kristianna Chung

18. June 2020

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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