Senior Quantitative Analyst
Robin Powered
 Boston, MA
At some point you’ve probably attended or scheduled a meeting -- maybe it was in a room, on video or over the phone -- then your meeting room gets stolen, you don't have the right resources, or you can't find where you need to be. Finding space and time to communicate can be painful. The problem is easy to understand but difficult to solve. That's where Robin comes in. We build software that coordinates meeting spaces, people and things in your workplace so you can get back to doing your best work.

Based in Boston, the DNA of our eclectic team shapes our culture in a way that means we can show up to work as our whole selves. Our values guide the way we treat our customers, our coworkers, and our candidates.  We are intentional with our words and actions. We’re helpful. And at the end of the day, we're all united by the mission of modernizing the open office so that businesses (like HubSpot, Twitter, Bumble and Kayak) are more enjoyable places to walk into each day, including our own.

Robin is looking for a Senior Quantitative Analyst to get in at the ground level and help us define best practices so we can use data to make smarter business decisions. We currently use multiple systems and need stronger technical collaboration to scale our business to enable cleaner solutions. As our go-to-market team aims to be more proactive than reactive, we need clean data to develop predictive analytics that drives both structured and unstructured data solutions. If you are an analytical problem solver who enjoys solving complex business problems using big data then we should chat.

Work you’ll be responsible for:

  • Develop and own comprehensive data warehouse to act as a central data repository for Sales and Marketing functions.  
  • Build alignment through accurate data reporting, systems integration and fluid models to enable automated and consistent reporting structure.  
  • Building predictive models and forecasting for the Go-to-market team.
  • Partnering with Engineering teams to improve Systems integrations for delivering and extracting structured and unstructured data effectively.
  • Providing analytics and solutions to different business problems by leveraging data to support outcomes.
  • Ability to work with multiple systems ranging from to Billings Systems and leveraging BI platforms to produce accurate data.  
  • Building machine learning and AI design from the ground up to drive Revenue metrics.
  • Coding and integrating multiple systems to drive robust reporting.

You are:

  • Collaborative. You enjoy partnering with various team to drive analytics on business trends through multiple systems.
  • Adaptable. You are able to work on multiple projects and love a fast-paced environment
  • Analytical. You’re passionate about using and understanding data to problem-solve.
  • Articulate. You are able to communicate technical findings based on complex quantitative analysis in a clear, precise, and actionable manner.

Experience you already have:

  • 3-5 years of hands-on industry experience preferable in SaaS/ tech company.
  • Several years of programming in Python or R and a solid understanding of SQL.
  • An academic background in applied mathematics, operations research, machine learning, computer science or a related field. Bonus points for an advanced degree
  • Practical experience solving analytical problems using data-driven, quantitative approaches.
  • Very knowledge in machine learning techniques and algorithms, such as Logistic Regression, Linear Regression
  • Familiarity with algorithm scaling, complexity and parallelization techniques for addressing large data volumes using Hadoop, Spark, etc
We’re creating the smart office of the future. We’d love to have you be a part of it.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are ADA compliant and handicap accessible.