Data Scientist, Advanced Analytics

New York
Negotiable

Data Scientist, Advanced Analytics
Marketing & Advertising
New York, NY
$120,000 - $130,000 + Bonus + Benefits

A leading, fast-growing AdTech company is looking for an experienced Data Scientist, Advanced Analytics to successfully build a range of Machine Learning and Attribution models to meet business growth in New York City.

THE ROLE:

As Data Scientist, Advanced Analytics, you will partner with the company's Integrated Media and Emerging Business teams and lead the design building, and deployment of a wide range of Machine Learning and Attribution models to maximize growth for major clients. You will be responsible for:

  • Collecting, cleaning, and manipulation large amounts of client data using SQL
  • Building Machine Learning (i.e., Linear/Logistic Regression, Classification) and Attribution models (i.e., MMM, MTA) using Python and/or R
  • Performing media strategy optimization, A/B testing, and Customer Lifetime Value analysis
  • Building dashboards in Tableau & delivering valuable data-driven insights to various stakeholders

YOUR SKILLS & EXPERIENCE:

  • Progressive Advanced Analytics and/or Data Science experience in Media, tech, or eCommerce
  • Proficient in data collection, pipelining, and preparation using SQL and Google Analytics
  • Strong hands-on Predictive/Statistical modeling skills using Python and/or R
  • Strong knowledge of ML techniques (i.e., supervised, unsupervised) and models (i.e., regression)
  • Proficient in building Attribution models (i.e., Marketing Mix, Media Mix, Multi-Channel/Touch)
  • Strong Customer Lifetime Value (CLV) and Customer Journey Analysis skills
  • Strong dashboarding/data visualization skills using Tableau, Looker, and/or Power BI
  • Strong verbal/written communication and presentation skills across the business
  • Bachelor's degree in Economics, Mathematics, Statistics, or related discipline; Master's or Ph.D. preferred

BENEFITS:

As Data Scientist, Advanced Analytics, you can make up to $130,000 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:

Advanced Analytics, Data Science, Marketing Attribution, Attribution Model, Multi-Channel Attribution, Multi-Touch Attribution (MTA), Machine Learning (ML), Supervised Machine Learning, Unsupervised Machine Learning, Logistic Regression, Linear Regression, Classification, Decision Trees, Segmentation, A/B Testing, Python, R, SQL, Tableau, Google Analytics, Marketing & Advertising, Marketing Mix Model

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00058/GL
New York
Negotiable
  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: 12th - 16th 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.      Express Pharma: The five biggest data challenges for life sciences Life Sciences has grown exponentially over the past 12 months. As the COVID-19 pandemic devastated the world, Life Science companies were in a race against time to create a life-changing vaccine and help us all back on the road to recovery.  In 2019, the Life Science market was valued at around $7.5bn. After this year’s influx of activity, the market is estimated to grow by over double in the next decade, reaching $18bn by 2030.  However, despite the positive growth the industry has had, this doesn’t mean Life Sciences will be free of challenges. In fact, with such a spike in the amount of data held by so many Life Science companies as they tried to work on a vaccine, data storage is now one of the main concerns for anyone working within the field.  In this article by Express Pharma, Vimal Venkatram, Country Manager for Snowflake India, highlights the five key data hurdles Life Sciences will continue to have to overcome in the following decade. These include data performance, data exchange and collaboration, data quality, data management and scaling, and regulatory compliance.  Read the full story here.  Harnham: How can organisations tap into the huge pool of neurodiverse data talent? For many companies, the past year has led to an increased focus on diversity and inclusion within businesses – a fantastic step forward. However, when we think of diversity, we usually assume people are talking about gender, ethnicity, sexuality and perhaps even physical disability. One area that is regularly missed from discussion is that of neurodiversity.  An umbrella term coined by sociologist, Judy Singer, neurodiversity can cover a wide range of neurological conditions such as dyslexia, autism, ADHD, ADD and dyspraxia. Our head of internal recruitment, Charlie Waterman, explores why neurodiverse talent shouldn’t be overlooked, and how Data & Analytics specifically can do more to tap into and harness this incredible pool of talent.` Exploring how employers can create a smooth recruitment process, successful onboarding programmes and retention schemes, this article highlights how all of this can be tailored to be accessible for anyone with an invisible disability. To read more on this topic, click here. Computer Weekly: What has a year of homeworking meant for the DPO? Employers in a significant number of industries across the world have had to uproot from the office to working from home because of the COVID-19 pandemic. For many of these employers, it appears that remote working, or a hybrid model of working, will become the norm post-pandemic.  But what has this sudden shift meant for the likes of Data Protection Officers (DPOs)? Most of these professionals have had to get to grips with managing and handling sensitive data from the comfort of their own living room. According to data from IBM, 70 per cent of DPOs believe that the shift to remote working will increase the likelihood of data breaches. So how can DPOs enjoy the benefits and perks of working from home, without the stress of poorly managed or breached data? In this article by Computer Weekly, steps are outlined on how DPOs can work closely with IT teams to minimise any data risk that could happen. This includes: Not allowing DPOs access to everything if it’s not necessaryDiscouraging local storage of dataRegularly reviewing security standards To read the full article, visit the website here.  Solutions Review: The three best Data Engineering books on our reading lists There’s no better feeling than getting stuck into a really good book. Not only can it be a great way to escape the stresses of everyday life, but by continuously absorbing new information, your knowledge on a specific subject can grow immensely.  Any branch of Data & Analytics, but especially Data Engineering, requires employees to always be thinking one step ahead, staying on top of new trends and keeping up to date with specific coding languages. While everyone learns in very different ways, reading is a brilliant education tool. Whether you’re a visual learner, an auditory learner or a reading learner, books and audiobooks could be the key to expanding your knowledge.  Solutions Review provides Data Engineers with three of the best books on the market at the moment to help you keep on top of your professional development. Data Driven Science and Engineering by Brunton and KutzData Engineering with Python by Crickard An introduction to agile Data Engineering by using data vault 2.0 by Graziano To read more about each of these books, 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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