You will join the small but tight-knit TIDAL Data Science & Analytics team, and will report into the Head of Data Science & Analytics. As a member of the team, you will help shape the future of our streaming and artist products. You will use your expertise in product analytics, experimentation and statistical modeling to empower data-driven decision-making in the full lifecycle of this
product development. You will collaborate closely with a wide array of cross-functional partners across Product, Engineering, Design and Data Engineering to drive impact for our users and our business.
This role is remote-friendly and open to candidates based anywhere in the United States Eastern Time Zone.
You Will
Work with cross-functional partners to translate business and product questions into analysis questions
Provide actionable data-driven recommendations to inform strategic decisions
Partner with engineering to ensure accurate and reliable logging for new product and features
Develop deep domain expertise and own measurement strategy for product and develop KPIs to track product success
Effectively communicate analysis and recommendations in verbal, visual and written form
Lead the design, analysis, and interpretation of experiments that shape decision-making
Develop and maintain curated analytics-friendly data sets using ETL
Build effective and reliable self-serve dashboards
You Have
4+ years of data science experience
A BS/BA in Math, Statistics, Computer Science, or a related technical field
Ability to translate ambiguous, unstructured problems into applicable data-driven analyzes
Fluency with data, analytics and visualization technologies (We use SQL, Tableau, Looker, and
Python)
Exceptional written and verbal communication skills with the ability to explain technical data to a variety of stakeholders across the company
Experience applying both statistical and machine-learning techniques to solve
practical product problems such as predicting churn, clustering user archetypes
Experience with experimental design and solid understanding of statistics
Experience building relationships to influence with product partners with data
Creativity in developing new processes, analyses, and recommendations
Familiarity with data warehouse design and best practices
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