Senior Data Scientist - Remote

New York
US$140000 - US$150000 per year

Senior Data Scientist
Marketing & Advertising
Remote or NYC
$130,000 - $150,000

Are you an experienced Data Scientist looking for more flexibility in your work place? If you have experience in omnichannel marketing, building statistical models in R/Python and want to work on customer focused analytics projects using open source technologies, then this is the role for you. A rapidly growing Marketing & advertising boutique agency are looking for an experienced Senior Data Scientist with proven capabilities in R/Python as well as coding in SQL to help them fully optimize and utilize their omnichannel data to its full potential, delivering insights to diverse stakeholders across the group.

THE ROLE - Senior Data Scientist

This is a unique opportunity for an experienced Data Scientist with hands-on experience in predictive/statistical modelling to join a growing team specializing in optimizing marketing and business growth as well as working on customer retention focused projects across multiple channels to really make their mark within this boutique firm.

  • As a Senior Data Scientist, you will be actively involved in being the project lead within a growing team responsible for deep dive analysis using R, SQL and Python for omnichannel marketing to understand customer behaviors and trends, as well as tracking customer channel traffic
  • Using your R and SQL/python skills you will be actively involved in building and developing statistical models from day one, including customer lifetime value, forecasting and propensity to churn models, helping the business utilize and optimize all marketing data for more efficient and effective targeting.
  • You will be seen as a voice of influence in the business taking full autonomy for your analysis and delivering insights and recommendations to senior management as well as non-technical audiences.

YOUR SKILLS AND EXPERIENCE:

  • Degree educated, preferably with a MSc in a numerical field such as Computer Science, Statistics, Math, Engineering or similar
  • Proven experience in an omnichannel marketing or customer analytics environment
  • Strong analytics capabilities in R/Python and SQL with the ability to build predictive and statistical models. Digital Analytics tools are desirable
  • Ability to deliver key insights and recommendations to senior stakeholders
  • Data driven with the desire to learn and develop new skills/analytics techniques

BENEFITS:

As a Senior Data Scientist, you can expect to earn up to $150,000 (depending on experience) + highly competitive benefits

HOW TO APPLY - Senior Data Scientist

Please register your interest by sending your Resume to Jenni Kavanagh via the Apply link on this page

KEYWORDS:

SQL, R, Python, Predictive Modelling, Attribution, Regression, Statistics, Marketing, Analysis, Customer Insight, Digital, eCommerce, Security, SaaS, Software as a Service, Stakeholder Management, Strategy, ROI, Campaigns, Direct Marketing, Online, Tableau, Optimization, Segmentation, Modeling, Advanced Analytics, Market Mix Modeling, Media Mix Modeling, Customer Lifetime Value, Propensity, Cluster, Predictive Models, Predictive Analysis

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85425/JK
New York
US$140000 - US$150000 per year
  1. Permanent
  2. Statistical Analyst

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Harnham blog & news

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Visit our Blogs & News portal or check out our recent posts below.

Weekly News Digest - 18th-22nd Jan 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. KDNuggets: 20 core Data Science concepts for beginners The field of Data Science is one that continuously evolves. For Data Scientists, this means constantly learning and perfecting new skills, keeping up to date with crucial trends and filling knowledge gaps.  However, there are a core set of concepts that all Data Scientists will need to understand throughout their career, especially at the start. From Data Wrangling to Data Imputation, Reinforcement Learning to Evaluation Metrics, KDNuggets outlines 20 of the key basics needed.  A great article if you’re just starting out and want to grasp the essentials or, if you’re a bit further up the ladder and would appreciate a quick refresh.  Read more here.  FinExtra: 15 DevOps trends to watch in 2021 As a direct response to the COVID-19 pandemic, there is no doubt that DevOps has come on leaps and bounds in the past year alone. FinExtra hears from a wide range of specialists within the sector, all of whom give their opinion on what 2021 holds for DevOps.  A few examples include: Nirav Chotai, Senior DevOps Engineer at Rakuten: “DataOps will definitely boom in 2021, and COVID might play a role in it. Due to COVID and WFH situation, consumption of digital content is skyrocket high which demands a new level of automation for self-scaling and self-healing systems to meet the growth and demand.” DevOps Architect at JFrog: “The "Sec'' part of DevSecOps will become more and more an integral part of the Software Development Lifecycle. A real security "shift left" approach will be the new norm.” CTO at International Technology Ventures: “Chaos Engineering will become an increasingly more important (and common) consideration in the DevOps planning discussions in more organizations.” Read the full article here.  Towards Data Science: 3 Simple Questions to Hone Python Skills for Beginners in 2021 Python is one of the most frequently used data languages within Data Science but for a new starter in the industry, it can be incredibly daunting. Leihua Yea, a PHD researcher at the University of California in Machine Learning and Data Science knows all too well how stressful can be to learn. He says: “Once, I struggled to figure out an easy level question on Leetcode and made no progress for hours!” In this piece for Towards Data Science, Yea gives junior Data Scientists three top pieces of advice to help master the basics of Python and level-up their skills. Find out what that advice is here.  ITWire: Enhancing customer experiences through better data management From the start of last year, businesses around the globe were pushed into a remote and digital way of working. This shift undoubtedly accelerated the use of the use of digital and data to keep their services as efficient and effective as possible.  Derak Cowan of Cohesity, the Information Technology company, talks with ITWire about the importance of the continued use of digital transformation and data post-pandemic, even after restrictions are relaxed and we move away from this overtly virtual world.  He says: “Business transformation is more than just a short-term tactic of buying software. If you want your business to thrive in the post-COVID age, it will need to place digital transformation at the heart of its business strategy and identify and overcome the roadblocks.” Read more about long-term digital transformation for your business here.  We've loved seeing all the news from Data and 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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