The energy of a newsroom, the pace of a trading floor, the buzz of a recent tech breakthrough; we work hard, and we work fast - while keeping up the quality and accuracy we're known for. It's what keeps us inventing and reinventing, all the time. Our culture is wide open, just like our spaces. We bring out the best in each other through collaboration. Through our countless volunteer projects, we also help network with the communities around us, too. You can do amazing work here. Work you couldn't do anywhere else. It's up to you to make it happen.
What's the role?
Bloomberg's Management and Enterprise Business intelligence team continually improves our Product and Sales teams' ability to make data-driven decisions through speed, transparency, and extraordinary customer service. We firmly believe our people are at the core of our success as they arm our client-facing teams with data to optimize their prospecting and retention efforts.
We enable Product teams to build better by providing them with deep insights into customer behavior. We help the News organization identify and explain long-term trends in readership, and we provide senior management with predictive analytics to improve revenue forecasting. And - that's just a sampling of projects from a typical week!
We're looking for a Senior Marketing Data Science Analyst to join our team. You love to uncover insights using data, and balance quantitative and qualitative skills to solve real business challenges. You are excited about partnering with marketing, product, and sales managers to help them better understand their audience and opportunities for growth - from basic function usage to deeper analysis that identifies new methods for driving adoption and identifying leads.
We are passionate about using data and analysis to help find opportunities through narrow targeting, profiling, and segmentation and partner with product managers, COOs, CFOs, Sales, and more to promote adoption, retention, and deeper engagement with our products.
We'll trust you to:
• Generate ideas and institute repeatable processes that support sales, adoption, and engagement of our products to drive usage
• Analyze large data sets to develop critical insights, such as identifying target audience segments for lead generation and/or marketing campaigns, and collaborate with business units on long-term planning, policy development, and problem resolution
• Operate as an internal consultant for our business teams in addressing strategic and operational issues, as well as handle ad hoc requests and special projects that may interface across multiple business units, subject areas, and departments around the usage and product adoption
• Understand our business drivers, assess, and scope projects, incorporate relevant levers, and prepare data driven recommendations on business strategies with the appropriate levels of detail
• Work on internal and external communications as needed for Senior Leader and Management Committee presentations, special events, and other key constituent interactions
• Operationalize individual and business performance metrics and report on deliverables through monthly/quarterly/annual reporting
You'll need to have:
• A minimum of 3 years' progressive professional hands-on experience in analytics, multivariate modeling, segmentation, quantitative methods, database, and digital data
• A minimum of 3 years' advanced SQL skills and experience working with relational databases, knowledge of big data systems such as Hadoop
• Prior experience with Python
• Experience in Marketing Analytics at a B2B company, with a comprehensive understanding of marketing measurement, Marketing Mix Model, channel attribution, and ROI frameworks.
• Experience with data visualization software tools (e.g. Qliksense, Tableau, Power BI, etc.) and the shown ability to learn and master new tools quickly
• Knowledge of google analytics a plus
• Ability to work with and explain technical concepts to non-technical partners, and break down sophisticated business problems into achievable steps
We'd love to see:
• Previous experience with PySpark
• Prior experience with R or Python
• Experience with command line and GIT
• Experience creating machine learning models and deploying into production
• Experience with experimental design
