R Programmer

Location: Mountain View, CA

Department: Professional Services

Type: Full Time

Min. Experience: Mid Level

Location: Mountain View, CA / Dallas or Austin, TX / New York, NY
Travel: Up to 70%

The Professional Services team has openings for client facing R Programmers to assist clients with their analytics needs: building analytics based solutions using Revolution Analytics’ products, embedding analytics into existing products, and scaling clients’ analytics needs.

Primary Responsibilities
The R Programmer is responsible for the technical requirements, design, and delivery of simple to large-scale analytics solutions.

The ideal candidate is an experienced R Programmer who enjoys a very dynamic work experience with bleeding edge high performance analytics technologies. Must thrive in the Start Up mindset where charting new territory is the norm.  Problem solving, learning in the field, translating technical concepts for the business user, and leadership are critical to this position.

The role has following core functions:

  1. Support onsite client project delivery
  2. Design and development of technical architecture, requirements, statistical model and take accountability of technical validity of the customer solution including installation and integration
  3. Pre-sales support of Sales and Sales Engineering teams to promote and position Revolution Analytics solutions as well as contribute to customer proposals
  4. Drive field developed innovation and IP back into R&D and work collaboratively with Technical Support, Sales, and Training teams
  5. Assist with RFI/RFP technical responses.
  6. Support R User Groups as requested.


  • Bachelor’s degree required with emphasis in Mathematics, Statistics, Computer Science, Engineering or Econometrics;  Master’s and Ph.D. preferred
  • Strong Programming Experience with R
  • Experience working in Windows and Linux environments
  • Must have developer level skills in SQL scripting and coding and familiarity with relational databases
  • Working knowledge of multivariate regression, time series models, cluster analysis, logistic regression, factor analysis and neural network models
  • Strong facilitation and communication skills
  • Ability to travel as required


  • Experience with SAS/SPSS/Matlab
  • Programming knowledge and experience with Hadoop/Map Reduce, and Java or .NET
  • Experience and familiarity with business intelligence and data warehouse design including ETL processes and tools
  • Experience working with clusters/grids preferred (e.g., Hadoop, Microsoft HPC Server, and/or Linux cluster job schedulers)
  • Experience working with Data Warehouse tools such as Teradata and IBM Pure Data for Analytics/Netezza
  • Ideal candidate will have consulting experience
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Cover Letter
Please provide information about your highest level of education:
1. School/College
2. Degree
3. Specialization
4. Year of Graduation*
Please provide information about your next level of education (eg. Masters):
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2. Degree
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4. Year of Graduation
Please provide information about your next level of education (eg. Undergraduate):
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2. Degree
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4. Year of Graduation
Areas of Domain Expertise*
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Number of years of R programming*
Education/Training in R*
Packages you are familiar with*
Packages you are expert in
Packages you have authored
Other programming languages you are familiar with
Databases you worked in R, e.g. Oracle, Sybase, SQL Server, MySQL, Netezza, Teradata, DB2, etc.
Describe your experience in using R with Hadoop (if any)
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Please rate your expertise with R on a scale of 1 to 10
10 = you literally have written a book or an R-package
7,8,9 = expert, go-to person on this technology
5,6 = solid daily working knowledge. Highly proficient.
3,4 = comfortable working with this, have to check manual on some things.
1, 2 = have worked with it previously but either not much, or rusty

1. Bayesian Inference
2. Chemometrics and Computational Physics
Clinical Trial Design, Monitoring, and Analysis
Cluster Analysis & Finite Mixture Models
Probability Distributions
Computational Econometrics
Analysis of Ecological and Environmental Data
Design of Experiments (DoE) & Analysis of Experimental Data
Empirical Finance
Statistical Genetics
Graphics & Visualization
High-Performance and Parallel Computing with R
Machine Learning & Statistical Learning
Medical Image Analysis
Multivariate Statistics
Natural Language Processing
Official Statistics & Survey Methodology
Optimization and Mathematical Programming
Analysis of Pharmacokinetic Data
Psychometric Models and Methods
Reproducible Research
Robust Statistical Methods
Statistics for the Social Sciences
Analysis of Spatial Data
Survival Analysis
Time Series Analysis
gRaphical Models in R
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