Manager, Data Science

New York
US$140000 - US$160000 per annum

Manager, Data Science
eCommerce
Greater New York | Remote
$140,000 - $160,000 + Bonus + Benefits

A leading consumer goods company is seeking an experienced Manager, Data Science to leverage machine learning models and bayesian techniques to meet business growth.

THE ROLE:

As Manager, Data Science, you will work closely with the Head of Advanced Analytics & Data Science in enhancing the eCommerce division's advanced analytics and machine learning capabilities. You will be responsible for:

  • Collecting & cleaning large amounts of consumer/third-party data (i.e., website, clickstream)
  • Building predictive models in Python (i.e., propensity, random forest, lookalike, regression)
  • Performing A/B testing and handling existing machine learning models
  • Delivering data-driven, actionable insights to a variety of key stakeholders

YOUR SKILLS & EXPERIENCE:

  • Progressive Advanced Analytics & Data Science experience in eCommerce
  • Strong understanding of CPG, Retail, and Consumer Health industries
  • Proven experience in purchasing audiences, audience modeling, & propensity modeling
  • Proficiency using advanced analytics tools like Python, R, SQL, and Tableau
  • Hands-on experience in A/B testing, machine learning, and dashboarding
  • Strong knowledge of random forest, time series, regression, and lookalike modeling
  • Strong written/verbal communication, negotiation, and presentation skills
  • Strong educational acumen - Bachelor's degree in Computer Science, Mathematics, Statistics, or related field; Master's in Business Analytics or Data Science preferred

BENEFITS:

As Manager, Data Science, you can make up to $160,000 base salary (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, Audience Modeling, Advanced Analytics, Predictive Modeling, Python, R, SQL, Tableau, A/B Testing, Random Forest, Regression Model, Lookalike Model, Machine Learning (ML), Data Science, Bayesian Techniques, Multi-Touch Attribution (MTA), XGboost, Bayes Theorem, Conditional Probability, Media Mix Model (MMM), Statistical Model, Marketing Mix Model, Attribution Model

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00065/GL
New York
US$140000 - US$160000 per annum
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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: 10th - 14th May 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.       Personnel Today: Mental Health Awareness Week: Concerns up 24% from last year It was Mental Health Awareness week this week, and this year, the focus was on the theme of nature. Personnel Today revealed some worrying statistics on the back of research from Close Brothers into the state of the population’s wellbeing in 2021.  Reports of mental ill-health has increased by nearly a quarter since this time last year as a direct consequence of the stresses and strains of COVID-19. From yo-yoing in and out of lockdowns to extended periods of isolation, job uncertainty and illness, this year has been like no other and it’s most certainly taken its toll.  63 per cent of 16–34-year-olds report mental health worries, up a seventh from last year.For those who are 55+, this worry has risen by a third. In this piece, it is made clear that the underlying issue lies not only with COVID-19, but the lack of support given by employers. The research revealed that 70 per cent of employers don’t have a wellbeing budget in place, and only 8 per cent of firms invest more than £126 per employee each year in health and wellbeing.  To read the full research, visit Personnel Today here.  Towards Data Science: 5 unique skills every Data Scientist should know We know that career tip articles for Data Scientists can all feel pretty ‘samey’. But this article in Towards Data Science mixes up the usual advice, looking at how those in, or aiming to be in, the industry need to brush-up on their softer skills if they are to be successful.  Tips include: Cutting down the jargon in order to communicate effectively with stakeholders. Don’t be hasty to overpromise, or you’re at risk of seriously under-delivering. Become friendly with your team’s software engineer, they’ll only be able to help you be more efficient and effective in your role.  Of course, there has to be some mention of coding in there – it wouldn’t be a data-based article without it. Make sure you’re mastering your SQL Optimisation. Don’t leave your Git out in the cold, become familiar with the practice to ensure you can update your model code quickly.  To read the full article, click here.  Analytics India Mag: What SMBs can learn from Big Tech’s AI playbook? AI has come on leaps and bounds in a short space of time, and its popularity has boomed. For the monster-sized companies, where budget is of no question and innovation can happen overnight if need be - embracing AI has been a total no-brainer. Workflows become more efficient, technology becomes smarter, and the scope of growth seems infinite.  However, despite all the benefits of AI that are so regularly shouted about, it’s been clear since the birth of the technology that there’s a huge divide in those who can and those who cannot afford to implement this innovation.  Up until now.  In this piece from Analytics India Mag, author Ritka Sagar, highlights how SMEs are finally finding ways to become ‘inventive’ with how they implement and use AI systems without breaking the bank.  To read how SMEs are managing this, click here. Silicon Republic: For smart cities to work, they need to be neutral and objective The concept of a smart city seems like something out of a futuristic, sci-fi film but, in fact, they are closer to becoming a reality than we may think.  The idea being that urban areas use sensors and other electronic methods to collect data. From citizens to traffic, water supply networks to crime detection, all of these assets of life, and more, are monitored, data collected, and insights given to make ‘life’ more efficient.  On the surface, it’s all very cool, but there are, of course, worries that come with it. In this Silicon Republic article, Computer Scientist, Larissa Suzuki, discusses the importance of ‘neutral and objective’ smart cities if they are to work.  She says; “Data and services in smart cities must be neutral and objective when reporting information about the city environment. They should encompass the entire population and respect data licences, regulation and privacy laws,” she said. “In a similar fashion, the digital services and the backbone technology – including algorithms – should be free from any ideology or influence in their conception, operation, integration and dissemination.” To read more on the future of smart cities, visit Silicon Republic 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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