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Data Scientist, Revenue Optimization

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Job ID DATAS10578 Date posted 01/30/2021 Location Madison, Wisconsin Job Type Full Time

Summary:  The Data Scientist is responsible for developing and refining models to enable Revenue Management to optimize room revenue.  The Data Scientist will discover revenue opportunities, design models and experiments, deploy solutions, iterate, and measure success of those solutions.  

Responsibilities:

  • Create, implement, and iterate predictive and/or prescriptive models to drive revenue through price and conversion across revenue management, CRM, web, onsite revenue, and other critical commercial levers.
  • Leverage R, Python, or similar to execute analysis in cloud data warehouse (Snowflake) for large-scale analytical efforts.  Advanced knowledge of R or Python as well as statistical packages and libraries required. 
  • Build statistical models to understand and capitalize on revenue trends, including but not limited to price elasticity of demand (PEoD)
  • Build forecasts for demand; automate and deploy for daily consumption and comparison
  • Analyze customer acquisition channels and how those customers purchase.
  • Work with data infrastructure and web development teams to define the data collection and management needs and to productionize statistical/machine learning models
  • Create customer dashboards using visualization tools to communicate critical KPIs
  • Work collaboratively with stakeholders to develop clear, compelling reports of analytic results and insights.
  • Present findings and recommendations to the relevant department and team members, including assumptions, data problems, and future/continuing research guidance.
  • Stay abreast of new analytic methods and technologies and support development of these new capabilities internally.

Required Qualifications:

  • Bachelor’s degree in statistics, operations research, economics, mathematics, or related field.  Advanced Degree (M.S., PhD) highly preferred.
  • Advanced experience with one or more programming languages, including but not limited to Python and/or R.
  • Strong database knowledge, including both structured (SQL) and unstructured (NoSQL) environments.  SQL programming required.   
  • Demonstrated experience working with large data sets and applying statistical models, including predictive modeling, in an academic or professional setting.
  • Demonstrated experience with varied qualitative and/or quantitative research methods, as well as sampling methodologies and design of experiments.
  • Demonstrated ability to learn and retain information quickly.

Preferred Qualifications:

  • Experience in hospitality, ecommerce, or B2C industry strongly preferred.
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