Business Data Analyst
Job Title: Business Data Analyst
Job Type: Full Time
Location: Denver, CO
Department: Professional Services
RingCentral, Inc. is an award-winning global provider of cloud unified communications and collaboration solutions. More flexible and cost-effective than legacy on premise systems, RingCentral solutions empower today's mobile and distributed workforces to be connected anywhere and on any device through voice, video, team messaging, collaboration, SMS, conferencing, online meetings, contact center, and fax. RingCentral provides an open platform that integrates with today's leading business apps while giving customers the flexibility to customize their own workflows. We are a $650M company, disrupting the $100B business communications and collaboration market (Avaya, Cisco, Microsoft) with a killer product (Gartner MQ Leader last 3 years) and rapid growth (30%). We are now pulling away from the competition and leaving them behind as we scale our product revenues and product capabilities. We are looking for outstanding talent to help us scale our revenues to $1b and beyond in the coming years.
Job Description:
The ideal candidate is adept at using large business data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. They develop and monitor data quality metrics and ensure business data and reporting needs are met. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Responsibilities:
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from company databases to drive optimization and improvement of professional services operations and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Help define metrics/KPIs for managing professional services business and build automated dashboards that can display those KPIs for internal stakeholders at various level of management.
- Ask and answer the next question(s) after gathering and analyzing the data to make insights more powerful not just delivering what was asked for.
Qualifications:
- Minimum of 3 years of data science-related experience in a large-scale environment preferably for professional services organization.
- Data visualization skills (Tableau) Putting together visualizations that update automatically allows our team to only need to do something once, rather than on a daily/weekly/monthly basis. This role needs to be able to turn the results of a complicated SQL query into something that a non-technical person can easily understand.
- Technical background of using Salesforce, MAQL, SQL database/coding, advanced MS Excel skills, business Intelligence and data analytics tools, business statistics, Hadoop platform, data lake and data warehouse architecture.
- Understanding of operations of Professional Service organization including: revenue and bookings; budgeting and forecasting; billing and accruals; planning and monitoring performance indicators.
- Strong interpersonal skills, ability to convey and relate ideas to others.
- Background in project or program management (PMP certification preferred).
- Bachelor's degree in a related field.