VP, Advanced Analytics & Data Science

New York
US$225000 - US$245000 per annum

VP, Advanced Analytics & Data Science
Marketing & Advertising
New York | Remote
$225,000 - $245,000 + Bonus + Benefits

Are you a hands-on, client-facing Advanced Analytics Leader who is passionate about translating data-driven insights into business value? A leading marketing consultancy is looking for an experienced VP, Advanced Analytics & Data Science to successfully build Marketing Mix Models and Multi-Touch Attribution Models to meet business growth.

THE ROLE:

As VP, Advanced Analytics & Data Science, you will be the Advanced Analytics Lead in fully owning the development of a variety of predictive models (i.e., Marketing Mix, Multi-Touch), and synthesizing complex analysis into compelling to help clients drive revenue. You will be responsible for:

  • Collecting, cleaning, and manipulating large amounts of customer data using SQL
  • Building predictive models in statistical modeling tools such as Python, R, and/or SAS
  • Mentoring, managing, and retaining multidisciplinary project teams for major client accounts
  • Leading the upselling of new projects to new clients, and winning significant RFP's

YOUR SKILLS & EXPERIENCE:

  • Extensive, progressive Advanced Analytics and/or Data Science experience in Media/Marketing
  • Proficient in a wide range of Advanced Analytics tools such as Python, R, SQL, and Tableau
  • Proven hands-on experience building a variety of predictive models (i.e., Marketing Mix, MTA)
  • Core focus in marketing analytics, predictive modeling, and measuring marketing effectiveness
  • Proven experience managing multidisciplinary teams (i.e., marketing analytics, data science)
  • Strong account management, client management, and business development/sales acumen
  • Strong verbal/written communication and presentation skills across the business
  • Proven experience owning/managing P&L, and winning significant RFP's
  • Master's degree in Data Science, Economics, Mathematics, Predictive Analytics, Statistics, or related field; Ph.D. preferred

BENEFITS:

As VP, Advanced Analytics & Data Science, you can make up to $245,000 base (depending on experience).

HOW TO APPLY:

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

KEYWORDS:

Econometrics, Advanced Analytics, Data Science, Predictive Model, Predictive Analytics, Statistical Model, Python, R, SQL, Tableau, Marketing Mix Model (MMM), Media Mix Model, Multi-Touch Attribution (MTA), Marketing Analytics, RFP, P&L, Business Development, Client Management, Statistical Analysis

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00048/GL
New York
US$225000 - US$245000 per annum
  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: 5th - 9th April 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.    The Drum: How data visualisation turns marketing metrics into business intelligence Gathering data is just one part of a marketer’s job but having the ability to turn this data into something visually stunning, informative and easy to use is another skill completely.  Marketers, on the whole, are extremely visual learners along with around 65 per cent of the population. Most of us are able to absorb data more effectively if the information being presented to us is done in such a way that is pleasing to the eye. And this is why Data Visualisation exists; it allows us to group, organise and represent data sets in a way that allows us to analyse larger quantities of information, compare findings, spot patterns and extract meaningful insights from raw data. Not only does Data Visualisation allow us to learn more effectively, but we can then turn this understanding into much broader and deeper Business Intelligence.  To read more on the positives of Data Visualisation and how to translate this into meaningful Business Intelligence, click here.  ZDNet: The five Vs of customer data platforms According to ZDNet, Customer Data Platforms (CDPs) are the hottest marketing technology today, offering companies a way to capture, unify, activate, and analyse customer data. Research done in 2020 by Salesforce showed that CDPs were among the highest priority investments for CMOs in 2021. If you’re planning to invest in a CDP this year, what five critical things do you need to think about when developing a successful strategy? ZDNet tells all.  Velocity - Your systems need to manage a high volume of data, coming in at various speeds.Variety - Every system has a slightly different main identifier or "source of truth," and the goal is to have one. This starts with being able to provision a universal information model, or schema, which can organize all of the differently labelled data into a common taxonomy. Veracity - Companies must ensure they can provision a single, persistent profile for every customer or account.Volume - It has been theorized that, in 2020, 1.7MB of data was created every second for every person on Earth. If you want to use those interactions to form the basis of your digital engagement strategy, you have to store them somewhere. Value - Once you have a clean, unified set of scaled data – now’s the time to think about how to derive value from it.  To learn more, read the full article here. Towards Data Science: How to Prepare for Business Case Interview Questions as a Data Scientist When you think of Data Science, the first thing that comes to mind will be technical knowledge of coding languages and fantastic statistical ability; softer skills such as communication and exceptional business knowledge may be overlooked. However, this is where many budding Data Scientists trip up. It is these softer skills and business acumen that sets brilliant candidates apart from others.  But how, when not usually taught at university, do you gather the business knowledge that will set you apart from the competition and showcase it in interview? Towards Data Science shares a few key pointers. Build a foundation – Brush up on your business basics. Research project management methodologies, organisational roles, tools, tech and metrics - all are crucial here. Company specifics – Research your company and its staff. Make sure your knowledge is tailored to the company you’re interviewing for. Products – This is where you’ll stand out above the rest if you get it right. The more you can know the ins and outs of products and metrics at the company, the more prepared you will be to answer business case questions. Read the full article here.  Harnham: Amped up Analytics: Google Analytics 4 Joshua Poore, one of our Senior Managers based in the US West division of Harnham, explores Google’s new and improved data insight capabilities, predominantly across consumer behaviours and preferences.  This exciting new feature of Google was born in the last quarter of 2020 and has now fully come into its infancy, and it’s an exciting time for Data & Analytics specialists across the globe. Joshua explores four key advantages of Google Analytics 4.0. Combined data and reporting - Rather than focusing on one property (web or app) at a time, this platform allows marketers to track a customer’s journey more holistically. A focus on anonymised data - By crafting a unified user journey centred around machine learning to fill in any gaps, marketers and businesses have a way to get the information they need without diving into personal data issues.Predictive metrics - Using Machine Learning to predict future transactions is a game changer for the platform. These predictive metrics for e-commerce sites on Google properties allow for targeted ads to visitors who seem most likely to make a purchase within one week of visiting the site. Machine Learning driven insights - GA4 explains it “has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms.” Machine Learning-driven insights include details that elude human analysts.  To read Joshua’s full insights on GA4, click here.  We've loved seeing all the news from Data & 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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