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Lead Digital Data Scientist

New York
Negotiable
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Lead Digital Data Scientist
Tech
New York
$155,000 - $165,000

Are you passionate about joining an innovative company that has a strong presence in major cities across America? A well-funded tech startup is looking for an experienced Lead Digital Data Scientist to spearhead the successful Multi-Touch Attribution Modeling to drive business growth in New York.

THE ROLE:

As Lead Digital Data Scientist, you will be the Marketing Data Science lead in owning the tracking, measurement and modeling of acquisition, attribution, and campaign performance across different marketing channels to improve marketing effectiveness. You will be responsible for:

  • Collecting & cleaning large amounts of customer data using SQL and Google Analytics
  • Building predictive models using Python and/or R (i.e., MMM, MTA, Propensity, Lookalike)
  • Building dashboards using Databricks, Looker, Power BI, or Tableau for senior management
  • Serving as a key Order Attribution & Predictive Analytics advisor to various stakeholders

YOUR SKILLS & EXPERIENCE:

  • Progressive Order Attribution & Predictive Analytics experience in eCommerce or tech
  • Proven experience with Media Buying, Multi-Touch Attribution, and Media Mix Modeling
  • Strong Digital Analytics skills in Google Analytics and Google Tag Manager
  • Proficiency with Advanced Analytics tools such as Python, R, and SQL
  • Strong dashboarding & reporting skills using Databricks, Looker, Power BI, or Tableau
  • Proven commercial experience handling Paid, Search, Social, Display, and Affiliate data
  • Strong verbal/written communication and presentation skills across the business
  • Bachelor's degree in Computer Science, Mathematics, Statistics or related, Master's preferred

BENEFITS:

As Lead Digital Data Scientist, you can make up to $165,000 base (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:

Propensity Modeling, Order Attribution, Multi-Touch Attribution (MTA), Media Mix Modeling, Market Mix Modeling (MMM), Python, R, SQL, Databricks, Tableau, Looker, Power BI, Google Analytics, Google Tag Manager, Data Science, Performance Marketing, Media Buying, Audience Modeling, Lookalike Modeling

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38999/GL
New York
Negotiable
  1. Permanent
  2. Marketing Analyst

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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.

Weekly News Digest: 4th-8th Jan 2021

Happy New Year! 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 and analytics.  TechRepublic: How IT can prepare for the coming hybrid work environment As the world continues to feel the pressures of COVID-19 , remote working is no longer the temporary and novel approach to work that we had envisaged. Vaccines are being approved and healthcare professionals are supporting its rollout across the globe. And, as each dose is administered, we move one step closer to what is likely to become a hybrid working situation. It is therefore pressing for tech leaders to prepare for a shift to this style of work. TechRepublic have explored how these leaders need to ensure that their technology is agile enough to support the needs of the workforce. Yet they also need to look beyond the tech, to redefine how teams work together. Read the full article here. Forbes: 350 CMOs: 3 Marketing Supertrends For 2021 ... And The No-Hype Future Of Marketing Tech We’re a big fan of this piece from John Koetsier, writing for Forbes. He describes how the marketing trends of the year ahead will take a focus on the holistic transformation in a digital-first world. Drawing on the thoughts of a range of Chief Marketing Officers, Koetsier explores that a mixture of new, emerging technologies will see the evolution of marketing to put digital right at the core. Openpath CMO Kieran Hannon, “Now meaningful customer-centric digital transformation can accelerate.” Suzanne Kounkel, Chief Marketing Officer for Deloitte, “Fusion is the new ecosystem. Fusion is the art of bringing together new business partnerships, customer insights, and digital platforms to create ecosystems.” Tristan Dion Chen, CMO of University Credit Union, “It is without a doubt crucial to recognize how COVID-19 has ushered in a strong sense of empathy as a driving force within the marketing industry.” The marketing industry is set to experience continued innovation and growth. Read more on this here. ZDNet: Facial recognition: Now algorithms can see through face masks Last year was a year unlike any other. The complete shift in the way we have had to go about our day-to-day lives, brought about by the ongoing implications of the COVID-19, is still being felt now. One of these changes to our lives is the compulsory requirement to wear a face mask when leaving home. Now, of course, this requirement has brought up some challenges for using our technology, such as banking and payment applications, which need facial recognition to activate it! However, ZDNet have reported that algorithms can now see-through face masks (pretty sweet, right?) The US Department of Homeland Security has carried out trials to test whether facial recognition algorithms could correctly identify masked individuals. This could be a real support for travel, banking and mobile technology in the future. Read more on the trial here.  Towards Data Science: Predicting the outcome of NBA games with Machine Learning The NBA season is back and well underway. Will the Los Angeles Lakers take the top spot again this year? Lots of fans will be making their own predictions as the season begins, but new research has been used to help predict the outcome of NBA games – with the help of the insightful tech that is machine learning. Focusing on five core steps, the team at ‘Towards Data Science’ used Big Data Analytics to help them predict the outcome of games: Scraping Relevant DataCleaning and Processing the DataFeature EngineeringData AnalysisPredictions Through the research, they found that the best published model had a prediction accuracy of 74.1 per cent (for playoff outcomes), with most others achieving an upper bound between 66–72 per cent accuracy. That’s scarily good! Click here to read more on the study and see the statistics in action. 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.

Sam Maughan

Sam Maughan

08. January 2021

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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

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