Senior Director, Data & Data Science

New York
US$180000 - US$200000 per annum

Senior Director, Data & Data Science
Internet
Toronto, ON | New York, NY | Remote
$180,000 - $200,000 + Benefits + Equity

Are you passionate about joining a mission-driven, high-growth organization that is revolutionizing the luxury goods industry? A leading Canadian direct-to-consumer eCommerce company is looking for an experienced Senior Director, Data & Data Science to successfully build the data roadmap and influence strategy to meet business growth.

THE ROLE:

As Senior Director, Data & Data Science, you will report to the President of the business and be the Data & Analytics Lead to develop the data pipeline & roadmap to execute on business strategy. You will be responsible for:

  • Lead data governance, data quality, data integration, and dashboarding
  • Spearhead overall long-term data strategy, road mapping, and support for other business units
  • Grow and manage a multidisciplinary team of data engineers, data scientists, and data analysts
  • Deliver data-driven, actionable insights to key stakeholders across the business

YOUR SKILLS & EXPERIENCE:

  • Progressive experience leading Data & Analytics teams at high-growth eCommerce companies
  • Experience leading data governance, data quality, data integration, data analysis & dashboarding
  • Proficient in data tools such as AWS, Google Big Query, SQL, and other related tools
  • Proficient in Business Intelligence tools such as Looker, Power BI, and/or Tableau
  • Well-versed in Advanced Analytics tools such as Python and/or R
  • Proven commercial experience building roadmaps and data pipelines
  • Strong ability to explain technical data & analytics concepts to non-technical stakeholders
  • Strong verbal/written communication, negotiation, and presentation skills across the business
  • Bachelor's degree in Computer Science, Economics, Engineering, Mathematics, Physics, Psychology, Statistics, or related field; Master's or Ph.D. preferred

BENEFITS:

As Senior Director, Data & Data Science, you can make up to $200,000 base salary (depending on your experience).

HOW TO APPLY:

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

KEYWORDS:

Data, Analytics, Data Engineering, Data Governance, Data Warehouse, Data Strategy, Roadmap, Digital Product, Advanced Analytics, Data Science, Machine Learning (ML), Amazon Web Services (AWS), Google Big Query, SQL, Python, R, Tableau, Looker, Power BI, Google Analytics, eCommerce, Direct-to-Consumer (DTC), D2C, Personalization, Pricing, Toronto, Ontario, Canada, Google Cloud Platform (GCP), NYC

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00086/GL
New York
US$180000 - US$200000 per annum
  1. Permanent
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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.

Data Analytics vs. Data Science: Which Should You Pursue?

Businesses are recognizing the increasing importance of data experts to help the company grow. As a result, the hiring demand for Data Scientists and Data Management Analysts has grown by 46% since 2019. This projection will only continue to rise in the next few years. So if you’re planning to become a data analyst or a data scientist, then here’s what you need to know. Data Analytics and Data Science: What's the Difference? Data Analysts and Data Scientists are both proficient in statistics and experienced in using database management systems. However, the key differences between these two professions revolve around their purpose for using the data. The Role of a Data Analyst These professionals organize and examine structured data to create solutions that will drive a business’ growth. They are tasked with studying sets of data using various tools, such as Excel and SQL, to uncover insights and trends that will serve as an answer to certain queries. For example, they can provide data-driven answers that can explain your marketing campaigns’ conversion rates or improve the logistics of your products. Then, they present these findings to concerned individuals and departments so they can formulate strategies that would boost revenue, efficiency, and other improvements. The Role of a Data Scientist Data Scientists are required to use their mathematical and programming skills to build statistical models that can provide solutions for a company’s potential problems. These professionals handle huge sets of both structured and unstructured data and prepare these for processing and analysis. They have to be very proficient in programming to utilize Predictive Analytics, statistics, and Machine Learning in unearthing meaningful insights from all the collected data. Their multidisciplinary approach towards data helps them draw conclusions that are valuable for specific business needs and goals. Career Paths for Aspiring Data Analysts Businesses, governments, and other institutions are on the search for individuals who are qualified in interpreting and communicating data. Data analysts are often offered huge salaries and great work benefits because the demand is so high and yet, the pool of talent is very limited. You can become qualified for a wide array of careers in data analytics through a comprehensive master’s degree program that will teach you how to interpret data and present actionable insights. These careers span from digital marketers to quantitative analysts. Graduates can work in governments and insurance companies as financial analysts who are in charge of assessing financial statements and economic trends to boost profit. On the other hand, you can also work as a marketing analyst whose responsibilities involve monitoring sales venues and evaluating consumer data. Their salaries range from $62,000 (Insight Analysts) to as much as $225,000 (highly paid Customer Analysts). Career Paths for Aspiring Data Scientists Data Scientists are experts in statistical analysis and in programming languages, such as Python and R. Thus, the average starting salary for professionals in this field is around $100,000 per year. Data Scientists would need to earn a bachelor’s degree and a master’s degree in computer science so that they would be adept at using complex software programs that are necessary for the position. If you’re more interested in software development, then you can work as a data engineer. These professionals create infrastructures that can gather and store data that analysts and other scientists may need to use. Data modellers, on the other hand, use techniques and databases to design and document data architecture. You can become a great asset to top companies in the US by pursuing a degree and a career in data analytics or data science. In this digital age, you can only expect that the demand for these positions would rise as data becomes increasingly important in driving business growth.  Written by Jena Burner for 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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