Develop scalable cloud-based data and analytics solutions, define success metrics, analyze complex business and customer data, and apply statistical modeling and predictive analysis. The role translates business questions into datasets and self-service analytics while partnering with sales, marketing, finance, and product stakeholders. Responsibilities include advanced SQL development, data modeling, visualization, documentation, stakeholder presentations, and delivering insights for strategic and operational decisions.
As a Data Analyst at Universal Audio, you’ll be a part of a high-impact Data & Analytics team that empowers data-driven decision-making throughout the company.
You will be a part of a small team tasked to design and build highly scalable modern cloud-based data solutions that help improve the customer experience and drive value for the business. You will work directly with business functions such as sales, marketing, finance, and product management to translate business requirements into data analytic solutions, using data and statistical modeling to inform strategic business decisions that impact the continued success of Universal Audio.
Key Responsibilities:
- Deliver solutions to support enterprise information management, business intelligence, data analysis, and other business interests
- Partner with key stakeholders to define success metrics and develop measurement frameworks
- Explore complex data sources, becoming a subject matter expert in all the data driving Universal Audio’s business, from marketing, sales, and customer behavior to web clickstream, and complex customer software and hardware use data
- You will translate complex business questions into analytic approaches and data sets that can drive a self-service model for analytic data access throughout our business
- Apply advanced statistical methods, data modeling, and predictive analysis to answer strategic and operational business questions
Requirements:
- Strong Python and/or R skills and familiarity with statistical and data science libraries (e.g., scikit-learn, pandas, matplotlib, ggplot2, tidyverse, carat, R stats, etc.)
- Some experience with software engineering best practices, including story estimation, test-driven development, code review, and version control with git
- Fluency with descriptive and inferential statistical techniques, including experimental design (DOE), linear and non-linear regression techniques, and time-series modeling.
- Commitment to collaboration and regularly presenting progress, insights, and recommendations to stakeholders
- Good technical writing and documenting
- Expertise in writing complex SQL queries and understand the methodologies to tune/improve query performance
- Knowledge of data modeling techniques for building and managing both physical and logical models
- Knowledge of data visualization tools such as Tableau or Looker
- Understanding of agile project approaches and methodologies
Experience and Education
- Bachelor’s Degree in a quantitative field
- 4+ years hands-on professional experience in Analytics and / or Data Science.
- 4+ years of experience with SQL databases, advanced SQL coding, and using Python and/or R for Data Analysis in a professional environment
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