Harnham
SEARCH
Shortlist

Manager, Data Product Analytics

New York
US$140000 - US$150000 per annum
Apply Save job

Manager, Data Product Analytics
Internet
New York
$140,000 - $150,000

Are you passionate about joining a cutting-edge company that actively helps some of the most popular brands as well as independent creators craft interactive video experiences through its platform? A leading tech company is looking for an experienced Manager, Data Product Analytics to successfully build strategy and execute the release of new data product offerings to meet business growth in New York.

THE ROLE:

As Manager, Data Product Analytics, you will be the lead in building data sets from scratch and will work closely with the Data, Business, and Product team leaders in developing the company's data product strategy. You will be responsible for:

  • Owning the data product roadmap and managing the full life cycle for new data features and products (e.g., requirements, specifications, design, implementation, delivery)
  • Working closely with analytics team and stakeholders in delivering new data products and data tracking solutions for various applications built on the company's platform
  • Serving as technical expert for various executives and engineering partners across advertising, commerce, and media

YOUR SKILLS AND EXPERIENCE:

  • Progressive product management experience in media, AdTech, or eCommerce
  • Proven commercial experience developing product specs, and writing dashboards & user stories
  • Strong communication and presentation skills (written and oral)
  • Proven experience in A/B testing and building product roadmaps from the ground up
  • Proficiency in technical tools such as SQL, Google Analytics, and Snowflake
  • Bachelor's degree in Mathematics, Statistics, Economics, Business, Engineering, or related field; Master's preferred

BENEFITS:

As Manager, Data Product Analytics, you can expect to make up to $150,000 (depending on your experience).

HOW TO APPLY:

Please register your interest by sending your resume to George Little via the Apply link on this page.

KEYWORDS:

Data, Product, Data Product, Analytics, SQL, Tableau, Power BI, Snowflake, Product Strategy, Product Roadmap, Media, Technology, Entertainment, Data Set, Data Tracking, A/B Testing, Dashboards, User Story, AdTech, eCommerce, SaaS, Product Spec, Technical Product, Product Analytics, Python, R, Product Lifecycle

Apply Save job
Send similar jobs by email
00009/GL
New York
US$140000 - US$150000 per annum
  1. Permanent
  2. Data Planning

Similar Jobs

Technical Product Manager, Machine Learning
Salary

US$130000 - US$140000 per annum

Location

Boston, Massachusetts

Description

Do you have product management experience in a SaaS and/or internet environment and have experience leveraging machine learning algorithms/models?

ADD TO SHORTLIST
Sr. Director, Head of Strategic Enterprise Analytics
Salary

US$215000 - US$225000 per annum

Location

New York

Description

Do you have extensive experience leading large Data & Analytics teams as well as working closely with C-suite executives?

ADD TO SHORTLIST
Business Insight Analyst
Salary

£35000 - £40000 per annum + benefits

Location

Preston, Lancashire

Description

An award-winning food manufacturer is looking for a Business Insight Analyst, who's ambition can match their own!

ADD TO SHORTLIST
Research Executive
Salary

£25000 - £32000 per annum + Benefits

Location

London

Description

This is an exciting opportunity to work on medical-based research projects to help deliver insight on a wide range of topics as a Research Executive.

ADD TO SHORTLIST

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.

Weekly News Digest: 22nd - 26th Feb 2021

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.  Search Engine Journal: 4 ways call tracking is changing (and why it’s a good thing) Call tracking is no longer about a customer seeing an ad, calling up the company, telling them how much they loved the ad and then deciding to purchase goods. This is a positive thing really because it wasn’t the most effective way for businesses to track how well adverts were doing anyway - who really remembers where they saw a billboard that took their interest, or what time of day an advert popped up on the TV? As call tracking technology becomes more advanced, call analytics have become much more accessible for all. Not only have they been able to transform how businesses of all shape and size advertise and track their success, but also how they market to potential audiences and track their sentiment.  This article from Search Engine Journal looks at the evolution of call tracking and call analytics from its most basic form, how it works now and what the future of this crucial set of analytics will look like in the future.  Read more on this here.  Towards Data Science: Data Science Year Zero Skills or qualifications in Data Science are becoming incredibly sought after by many employers, but the knowledge of how to break into the sector is still a little unclear for potential candidates. In this article by Towards Data Science, they break down the crucial elements of how to successfully enter the industry in four easy steps.  What the author, Bala Vishal, lacked when he started and how you can set off on a better footing.The most important skills and tools to have under your belt.Which skills should you home in on first.How to thrive in the workplace. This incredibly insightful piece should be a ‘must-read’ for any budding Data Scientist looking to break into Data in 2021 and beyond.  Read more here.  KD Nuggets: 10 Statistical Concepts You Should Know for Data Science Interviews This article is perfect for anyone in the Data Science industry. Whether you’re new to the game or looking to take the next step on the career ladder, make sure you brush up on these crucial statistical concepts you should know inside out before entering interview.  A few, in no order, include: Z tests vs T tests An invaluable piece of knowledge that will be used daily if you are involved in any statistical work.Sampling techniques Make sure you’ve got the main five solidified in your knowledge bank - Simple Random, Systematic, Convenience, Cluster, and Stratified sampling.Bayes Theorem/Conditional Probability One of the most popular machine learning algorithms, a must-know in this new era of technology.  Want to know about the other seven? Read more here. Forbes: 48 per cent of Sales Leaders Say Their CRM System Doesn’t Meet Their Needs. The Good News Is That This Is Fixable. This article by Gene Marks explores why teams aren’t happy with their current CRM systems, and how this can be remedied. New research from SugarCRM found: 52 per cent of sales leaders reported that their CRM platform is costing potential revenue opportunities.50 per cent of the companies said they cannot access customer data across marketing, sales and service systems.Nearly one-third complained that their customer data is incomplete, out of date, or inaccurate. While damning statistics, Marks then goes into how this worrying situation can be fixed for good. He says: “Like just about all problems in business, this problem comes down to two factors: time and money. The blunt fact is that most companies are not willing to spend the necessary time or money needed to enable their CRM systems to truly do what they’re designed to do. CRM systems are not just for sales teams. And they're not just for service teams. For a CRM system to be effective, a company must adapt it as its main, collaborative platform.” Read more on this here. We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

Ross Whatling

Ross Whatling

26. February 2021

News

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

News

Recently Viewed jobs