Lead Marketing Data Scientist

New York
US$160000 - US$175000 per year

Lead Marketing Data Scientist
Online Marketplace
New York City
$160,000 - $175,000

Are you obsessed with how we can use data to personalize customer experiences and optimize data for the wider business Do you like to innovate and combine highly advanced analytics techniques with customer-rich marketing data to develop processes that optimize marketing efficiencies and effectiveness, while focusing on new customer acquisition? If you are a hands-on Senior Data Science professional with a background in SQL and R/Python, and have extensive experience working with Marketing teams to personalize all engagement across social and digital channels then I have a great opportunity for you to join one the leading analytics teams in the US.

THE COMPANY:

A globally renowned online marketplace in the heart of New York City that has millions of rows of rich customer data, are looking for an experienced Data Science professional to come on board and work seamlessly with the marketing teams to help them optimize the media marketing mix, increase new customer acquisition, measure attribution, and make marketing more effective to focus on growth, all while delivering insights and recommendations on how they can maximize engagement with the company.

THE ROLE - Lead Marketing Data Scientist

As a Lead Marketing Data Scientist, you will be a hands-on individual contributor, using SQL, and Python/R to answer key business questions about how to personalize marketing, optimize channel engagement and increase customer acquisition, specifically in the digital and social arenas. You will be building media mix models, identifying what should be measured to determine success innovating processes to ensure customer loyalty, and product upsell/cross sell growth. You will:

  • Be the Data Scientist aligned with the Marketing team to set the strategic vision of advanced analytics surrounding marketing attribution, campaign effectiveness and customer lifetime value initiatives, prioritizing projects and ensuring that marketing personalization and advanced methodologies are at the forefront of all data-driven decisions made
  • Be hands on using Python/R and SQL, as well as Google Analytics and Facebook Marketing to measure the performance of all paid, search, social and display marketing initiatives, focusing on measuring and modelling customer acquisition, and delivering insights and recommendations
  • Work with a broad range of hard-to-capture data sets across both online and offline channels to understand customer behaviors, needs, wants and attitudes, turning these into insightful stories that can be used by the sales, marketing and operations teams to create new personalized services for the individual user

YOUR SKILLS AND EXPERIENCE:

  • Degree educated in Math, Statistics, Operational Research or similar numerical discipline, with a Masters preferred but not essential
  • Strong technical background in SQL, Python/R with solid experience in Multi-Touch Attribution, Media Mix Modelling, Customer Lifetime Value, campaign management and lead scoring across paid, search, social and display channels
  • Exceptional communicator, with proven capabilities in demonstrating your ability to turn highly complex analyses into stories and customer journeys that can be used by multiple technical and non-technical audiences
  • Background within an eCommerce or retail environment is highly desirable, with the ability to apply complex human behaviors and attitudes to enhance the development of the overall business

BENEFITS:

As a Lead Marketing Data Scientist you can expect to earn up to $175,000 + benefits (depending on your experience)

HOW TO APPLY:

Please register your interest by sending your resume to Jenni Kavanagh via the Apply link on this page

KEYWORDS:

SQL, Python, R, Analytics, Strategy, Data Science, Marketing, Media Mix Modelling, Multi Touch Attribution, Paid, Search, Social, Display, Google Analytics, Facebook Marketing, Product, Attribution, Customer Lifetime Value, Lead Scoring, Retail, eCommerce Advanced Analytics, Business, Time-Series, Regression, Statistical Analysis, Predictive Analytics, Model, Modell, Modeling, Modelling, Senior, Lead, Performance Marketing Data Science, Stakeholder Manager, Customer Acquisition, Looker, Retention, Sales, Growth, Tableau, AWS, Scala, Customer Behavior, A/B Testing

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VAC-241220/JK
New York
US$160000 - US$175000 per year
  1. Permanent
  2. Statistical 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.

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. 

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