Renovia is looking for a Principal Data Scientist.  This role is responsible for advising the business on the potential of data, providing new insights into the business’s mission, and, through the use of advanced statistical analysis, data mining, and data visualization techniques, creating solutions that enable enhanced business performance.

The Data Scientist also plays a role in the management of projects in support of the business where they are required to leverage and synthesize large volumes and variety of data in order to enhance the business’s understanding of individual population segments, propensities, outcomes, and decision points.

The Data Scientist combines data, computational science, and technology with consumer-oriented business knowledge in the business setting, to drive high-value insights into the business and drive high-impact through the business levers at the business’s disposal.

This role requires a self-starter who is willing to set direction and work independently to drive results.

Responsibilities

  • Working with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
  • Looking for order and patterns in data, spotting trends that can improve patient outcomes.
  • Collecting large amounts of unruly data and transforming it into a more usable format.
  • Solving business-related problems using data-driven techniques.
  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques 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.
  • Develop company A/B testing framework and test model quality.
  • Coordinate with different functional teams to implement models and monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.
  • Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
  • Communicating and collaborating with both IT and business.

 

 Qualifications

Personal

  • A drive to learn and master new technologies and techniques.
  • A passion for wanting to understand data and a desire to answer questions nobody has asked yet.
  • Excellent written and verbal communication skills for coordinating across teams.

 Education

  • BS in Computer Science or equivalent quantitative discipline.
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.

Experience

  • 5 years of development experience delivering software applications for machine learning and statistical analysis in a production environment.
  • MS/PhD studies in computer/data science can be substituted for 2/4 years of experience.

Technical Skills

  • Data Preparation: Use of ETL /Data Prep tools, knowledge of structured vs unstructured data
  • Analytics: Working with a variety of programming languages, including SAS, R and Python.
  • Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
  • Experience analyzing data from 3rd parties such as Google Analytics, Adwords, Facebook Insights, etc.
  • Experience with distributed data/computing tools: Hadoop, Hive, BigQuery, MySQL, etc.
  • Data Visualization: Experience with BI tools such as Tableau, Looker, PowerBI, etc.

Renovia Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.