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Principal Data Scientist - Paid Media
New York, NY
$160,000 - $170,000 + Benefits + Equity
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 Principal Data Scientist - Paid Media to spearhead the successful Audience Modeling to drive business growth in New York.
As Principal Data Scientist - Paid Media, you will be the Advanced Analytics 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:
YOUR SKILLS & EXPERIENCE:
As Principal Data Scientist - Paid Media, you can make up to a $170,000 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.
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, Audience Purchasing, Paid Search, Paid Marketing
US$145000 - US$155000 per annum
Do you have a background leading an Analytics or Data Science team at an eCommerce company and have a strong understanding of machine learning?
Do you have strong predictive modeling skills using Python or R and have a background working at either media or tech companies?
US$120000 - US$140000 per annum
Do you have proven experience managing an analytics team and have strong predictive modeling skills using Python or R?
£50000 - £65000 per annum
Milton Keynes, Buckinghamshire
This organisation operates within the finance and accountancy sector, offering its customers a community as well as the chance to grow, develop, and upskill.
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
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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. KDnuggets: 10 resources for data science self-study If you are interested in getting into data science, there are two basic pathways that you can opt for. There’s the traditional college degree route or the self-study option, the latter of which is growing in popularity among aspiring data scientists. This informative article from KDnuggets provides some insightful tips for data science self-study, grouped into three main categories: Resources for building fundamental knowledge; resources for data science practice; and resources for networking and continuous studies. Resources for building fundamental knowledge:Massive Open Online Courses (MOOCs)Learning from a TextbookYouTubeKhan AcademyResources for Data Science practice:KaggleInternshipsResources for networking and continuous studies:MediumLinkedInKDnuggetsGitHub Find out more here. Analytics India Magazine: How machine learning streamlines risk management Abhaya K Srivastava, SVP at Northern Trust Corporation, recently spoke at the Machine Learning Developers Summit 2021. Srivastava delved into how different sectors including financial, healthcare and retail are making use of emerging technologies like AI and Machine Learning. One of the main takeaways from the speaker session was discussions around how Machine Learning can support how organisations streamline their Risk Management. Srivastava stated, “It is essential for us to establish the rigorous governance processes and policies that can quickly identify when the model begins to fail.” He continued, “The terms of AI are not new, but businesses and organisations have started using these technologies in a different way. We have noticed the influence of machine learning in business applications, ML is playing an important role in Risk Management and there has been a constant focus on how risks are being detected, reported, managed.” There are a range of different machine learning techniques that can be applied to support risk management. It is the role of organisations, and their partners to discover how these processes can be applied. Read more on this here. Information Week: 3 Ways to Empower Female Software Engineers on Your Team We think this is a great article from Information Week that acknowledges the importance of establishing greater diversity and inclusion within software engineering, in particular to empower women in the industry. The article focuses on three areas: Create an inclusive team:Building an inclusive team is a strategic process and should include making sure everyone has a voice and that the workplace is a safe place to take risks.Provide a support system:Support establishes trust and shows a commitment to the well-being of your people. When leaders support their employees, it can significantly affect job satisfaction and performance.Enable women to inspire othersThe first thing to do is make sure the women in your organisation have a seat at the table; they should have a say in the decision-making process. Even if you have a good understanding of these, it’s important to keep educating yourself and the wider team in order to implements processes and strategies that make for a truly inclusive team. Read more on this here. TechRepublic: 8 must-read leadership books recommended by tech titans and innovators Are you looking for your next read to help you elevate your visibility and skill as a leader in the tech industry? Look no further, as TechRepublic have put together a list of leadership books recommended by notable leaders from within the industry. Here are a few: The Ride of a Lifetime (Robert Iger) - Recommended by Bill GatesDrop the Ball (Tiffany Dufu) - Recommended by Sheryl SandbergMindset (Dr Carol S. Dweck) - Recommended by Satya NadellsTrailblazer (Marc Benioff) - Recommended by Susan Wojcicki It’s valuable to have insight from leaders that are already leading the way for tech innovation in their field, inspiring and supporting future leaders to achieve great things too. Click here to read the full list of recommended leadership books from Bill Gates, Satya Nadella, Sheryl Sandberg, Tim Cook, and other notable industry leaders. 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 email@example.com.
19. February 2021
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.
19. March 2020