About Trusaic

Trusaic is a software company that refines disparate data from all sources, structured and unstructured. We combine expertise in data, expertise in regulations and analytics, and expertise in software to deliver regulatory compliance and actionable intelligence.

Since 2014, Trusaic has grown 700% in revenue with a World-Class Net Promoter Score (NPS) of 75. We've earned this world-class status through being customer-obsessed and meticulous by nature. We're all in with you, and there with solutions you can trust when it matters most. 

Love statistics?

Realize that Small Data is actually where the inferential action is? Trusaic's statistical assessments help companies achieve their Diversity, Equity, and You are proficient in coding and quality testing, and are comfortable in interpreting statistical results and presenting results for non-technical audiences. Trusaic's products help companies achieve their Diversity, Equity, and Inclusion (DEI) goals as well as navigate compliance across jurisdictions. Join Trusaic to apply both your statistical expertise and your R-programming skills toward social good.
Required:

  • Demonstrated proficiency in writing, running, and interpreting R code and output;
  • Conduct research to identify and apply relevant R packages for accomplishing programming objectives;
  • Extensively document own code;
  • Debug own code and the code of others;
  • Filter, sort, compute summary statistics, pivot tables and merge data in spreadsheets;
  • Rapidly revise code in response to changes in product specifications;
  • Troubleshoot
  • Work within the git version control system;
  • Meet agreed upon deadlines.

Qualifications:

  • Master’s degree in quantitative subject e.g., mathematics, statistics, computer science, economics or Bachelor's degree in same with 5 years work experience in a quantitative field
  • Successfully completed coursework in probability and statistics, including linear and logistic regression, maximum likelihood estimation, hypothesis testing, model assessment and selection, Bayesian inference, Monte Carlo methods

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