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Senior Marketing Data Analyst
Salary

US$80000 - US$95000 per year

Location

Boston, Massachusetts

Description

Looking for a skilled Senior Marketing Analyst to join a powerhouse financial services company.

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VP Analytics
Salary

US$185000 - US$210000 per year

Location

Boston, Massachusetts

Description

Are you a senior analytics leader looking for career growth in a fast-paced and agile environment?

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Director Advanced Analytics
Salary

US$155000 - US$175000 per year

Location

Hartford, Connecticut

Description

Are you ready for the next challenge within your advanced analytics career?

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Director of Analytics
Salary

US$160000 - US$180000 per annum

Location

New York

Description

The Director of Analytics will be a hands on leader responsible of leading analytics projects to grow the business and building the analytics team!

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Senior Data Analyst - Marketing Effectiveness
Salary

US$95000 - US$110000 per year

Location

New York

Description

Are you passionate about interactive media & how it can be used by a variety of audiences? If you're hands on working in marketing effectiveness, get in touch.

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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.

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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

Machine Learning and Customer Experience for Business Scalability

Over the last four decades, we’ve feared change. Technophobia. Cyberspace. Smart devices. AI, Robotics and Automation. Each of these transformative shifts have changed our lives in one way or another. But there is a new, unexpected and desperately needed change already in play; putting the human back in our lives.  Human Resources. Human-centric customer service. Humans in cooperation and collaboration with smart technology. Both in B2B and B2C businesses, putting the human back in focus is imperative to success.  Consider Netflix. How it began, how it’s evolved, and how its efforts are seemingly leading the way for next gen personalization. Think: If you like this, then you may like (insert service or product here). Amazon does much the same. Putting the Human Element Back in CX When you call customer service with a concern or problem. What happens? Either there’s no phone number at all and you’re forced to send an email which you hope gets read by a person. Or if you do call, you push buttons trying to figure out which branch of the tree will get you to the correct person.  Chatbots have been one answer but they really only alleviate acknowledgement. We’ve all called a customer service number and spoken to two or more people about our issue. Bill Paterson, EVP of Salesforce, suggests a four-point, human-centric customer service engagement strategy, to help solve the problem.  In addition, his article takes a deeper dive into putting the human back in customer service. At the heart of the matter is putting Emotional Intelligence, care, and empathy back into the equation. Technology may be how people reach out, but it’s a human they want to speak to and connect with. When the two are paired, there’s a much better chance of success. And repeat customers. Pairing Machine Learning with a Human-Centric Touch While strategies and metrics still have a big role to play, there are other ways to measure customer success. Data gathered from your customers will only get you so far, but the human element, the human connection, supported by technology, is the next shift in Digital Transformation.  Machine Learning models can help predict what customers will want or need, but meaningful customer relationships are just as vital. It’s this pairing which can generate great service and scalability of today’s modern business. Though there is a strong underpinning of engineering components in building models, only a portion involves code. Much of the effort goes into the pipeline and workflow systems and infrastructure. It’s at this systems level, Data Scientists can focus on design and implementation of production. This strategy ensures that before building good models, a good foundation must be laid. One portion of this workflow has been called the ‘art of Machine Learning’. The ‘Art’ of Machine Learning  Data Scientists and Machine Learning Engineers have any number of ways to solve a problem. Dealing with such vast amounts of Data within a model is not unlike determining how to scale for a website which needs to handle large fluctuations in web traffic. The nuances of technology within the realm of human experience is an artform. Though in the future, most engineering challenges will be automated and open-source will be a go-to framework. As tools improve and ETL processes improve, ML Engineers and Data Scientists will get the opportunity to focus more on models and less on systems. But beyond the artform of experimentation and intuition is the growing trend for soft skills in tandem with technical skills. Those who can lead a technical team, who can communicate to non-technical professionals, and still have the Emotional Intelligence to navigate the human psyche. It’s these individuals who will be ready for the next step in leading businesses into the next generation of customer service.  Ready to take the next step in your career? Take a look at 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.  

Leah Topham

Leah Topham

20. February 2020

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