Customer Success Data Scientist

Customer Success Data Scientist
Based at Rapid7 global headquarters in Boston, the Customer Success Operations team is looking for an entry level Data Scientist. A successful candidate will be a growth-minded and analytical individual who is passionate about working with data and making an impact.
As a data scientist, you will have the opportunity to work with data from multiple systems, product lines and cross functional stakeholders where you will help build an account health model to monitor customer health from scratch in a fast paced environment. You will be part of a team that builds tools, analysis and insights that informs the Customer Facing team's engagement with their customers. The account health model and metrics you build will help monitor customer health and improve product adoption and custom satisfaction in a proactive way.
In this role, you will:

• Develop/Consolidate disparate and large datasets for use in reporting, analytical, and modeling work from multiple sources including product usage, customer surveys, etc.
• Model and analyze datasets using descriptive and predictive analytics or methodology to identify how metrics correlate to customer health.
• Engage directly with internal data teams to ensure the data is accurately collected, measured and reported.
• Build Dashboards and reports in BI tools to present metrics visually and articulate findings to both technical and non technical audiences.
• Collaborate across multiple teams including data engineering and other internal technical teams

The skills you'll bring include:

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, or a related STEM field.
• 0-2 years of hands-on experience in data science, customer retention analytics or a similar role.
• Strong understanding of customer retention metrics and strategies in SaaS.
• Strong analytical skills and proficiency in building predictive models in any analytics tools or scripting language(e.g. Python/R, etc) and the ability to articulate the model to non technical audience.
• Ability to manage large volumes of data in any of the cloud computing platforms such as Snowflake or GCP.
• Experience with data visualization tools such as Tableau or Power BI to answer business questions.
• Ability to prioritize and meet deadlines within a dynamic, evolving portfolio of responsibilities in a fast-paced environment.
• Motivated with a strong desire to succeed, both individually and as part of a team.

Nice to have:

• Knowledge of Machine Learning models or natural Language processing
• Experience with Salesforce or other similar CRM platform
• Experience with github or familiarity with data engineering and data management tools and processes
• Basic knowledge of cybersecurity and performance KPIs.

We know that the best ideas and solutions come from multi-dimensional teams. That's because these teams reflect a variety of backgrounds and professional experiences. If you are excited about this role and feel your experience can make an impact, please don't be shy - apply today.
About Rapid7
Rapid7 (NASDAQ: RPD) helps organizations across the globe protect what matters most so innovation can thrive in an increasingly connected world. Our comprehensive technology, services, and community-focused research simplify the complex for security teams, helping them reduce vulnerabilities, monitor for malicious behavior, be in 10 places at once, and shut down attacks. We're on a mission to make security solutions easier to use and access so we can bring safety and resilience to more people. With more than 10,000 customers across 140+ countries, Rapid7 is a leader in cybersecurity that has earned numerous industry accolades and recognition for our technology and culture.
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All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or any other status protected by applicable national, federal, state or local law.