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Data Science Jobs in New York

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Data Scientist - Production Models
Salary

US$13500 - US$135000 per annum + Amazing Benefits

Location

New York

Description

This is an opportunity for an innovative Data Scientist to cut their teeth in a data-first, environments.

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ASSOCIATE DIRECTOR, R&D PORTOFLIO MANAGEMENT
Salary

US$170000 - US$190000 per annum + bonus and benefits

Location

Philadelphia, Pennsylvania

Description

Looking for an Associate Director to lead research and innovation with pharma! Must have a deep understanding of data science and strong business acumen.

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Principal Machine Learning Engineer
Salary

US$240000 - US$260000 per annum

Location

New York

Description

This is an opportunity for an experienced Machine Learning Engineer to come into a well-established start-up and build their infrastructure from the ground up.

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Senior Machine Learning Engineer
Salary

US$161000 - US$185000 per annum + Great Work Life Balance

Location

New York

Description

A global retail business is looking to bring in a machine learning engineer to work hands-on developing cutting edge machine and deep learning algorithms.

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Senior Data Scientist - Healthcare
Salary

US$150000 - US$170000 per annum

Location

New York

Description

This is an opportunity for an experienced Data Scientist to come into a well-established start up that just received over $150m in funding

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With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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Which Skillset Do You Need: Data Science or Advanced Analytics?

According to our 2020 US Data & Analytics Salary Guide, there has been a recent uptick in leadership roles within the Data & Analytics industry. Stemming from the skillset which is equally balanced between technical and communicative abilities, this field is fast approaching a 50/50 gender split. These are the leadership roles for which businesses are seeking employees who can translate business objectives into actionable insights, and yet, too often businesses think this is the role of the Data Scientist. As Data Science, Machine Learning, and other terms in the Data industry change to encompass new roles, it is imperative businesses understand what skillset they need to fill which role. So, here’s a quick comparison to help navigate which role has the best skillset for your needs. TRADITIONAL DUTIES OF A DATA SCIENTIST  Understand one or more coding languages such as R, Hadoop, SQL, Apache Spark, etc.Able to collect, gather, and analyze Data from past and current applications for business recommendations.Craft statistical models for planning and implementing strategies. While it’s true these three duties are similar to an Advanced Analytics Skillset, the advanced skillset takes things a bit further.  TRADITIONAL DUTIES OF A CHIEF DATA OR ANALYTICS OFFICER As this role leans a bit more toward the Advanced Analytics skillset, let’s take a look at where it jumps off from the traditional Data Scientist role. Create a Data Strategy and communicate the vision of that strategy.Create Data access policies and strategize with business executives. Oversee a variety of functions including Data Management, Data Governance. Whether the title is Chief Data Officer or Chief Analytics Officer, these are the high-level roles which might report to the CEO or COO rather than the CIO. The Chief Data Officer falls within the senior executive team and is responsible for not only the Data Strategy and its governance, but explaining its benefits in clear language to the other executives. WHEN BUSINESS & CANDIDATE EXPECTATIONS ALIGN  Though Advanced Analytics teams have remained strong during the pandemic, and the field is ever-changing as businesses understand which skillset they need for what job, there has been some turnover in the past.  So, why the turnover if it’s an expanding field? The answer is two-fold in that oftentimes, the candidate and business expectations aren’t aligned. And to that end, it often stems from businesses believing once they’ve hired someone their problems are solved.  However, when the right person with the right skillset is in the right place. And when businesses understand that person can help them solve the problem once there is a strategy and processes in place, then the two are more efficiently aligned.  WHERE TO LOOK FOR ADVANCED ANALYTICS & INSIGHT ROLES If you’re a candidate and have five years or more experience in the Analytics industry, there is a huge growth in demand from EdTech and TeleHealth enterprises. As businesses have gone online and virtual, it’s important to have someone in place who can navigate the changing nature of education and medicine within the Data & Analytics field. One of the key ingredients businesses hunger for are candidates who can blend statistical analysis with the communication skills. For businesses, candidates want to grow with the business and have the opportunity to make an impact. In our recently released 2020 Salary Guide we discuss each specialism; what’s working, what isn’t, and how businesses can hire and retain top talent to keep their projects on track and their businesses running smoothly. If you’re interested in Data and Technology, Risk or Digital Analytics, Life Science Analytics, Marketing and Insight, Data Science, or Computer Vision, we invite you to check out our current vacancies. If you’d like to learn more, contact one of our expert consultant. 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.  

Jenni Kavanagh

Jenni Kavanagh

30. July 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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