Intern - Product Development - Statistical Programming and Analysis
South San Francisco, CA

Job Description

Data Science Summer Intern, Statistical Programming & Analysis

Start Date: Summer 2019

Length of Assignment: 3 - 6 months (12 weeks minimum)

Work hours: 40 hours per week

Qualifications

* Current MS or PhD student, or recent graduate (<2 years)
• Preferred Major(s): Statistics, Biostatistics, Computer Science, Data Science

Statistical Programming & Analysis mission: delivering the scientific portfolio with smarter analytics

* To provide timely and accurate analysis and reporting for drug development and submission to health authorities
* To act as experts in clinical data, including manipulation, and analysis
* To maximize efficiencies by using standard processes, technologies, tools, and data formats
* To develop new tools and techniques to enable quicker exploration and scientific decision making, with a focus on building interactive applications
* To build partnerships with other analytical groups within the company to share knowledge and promote efficiency standards
* To build partnerships with biostatistics (design and analysis of clinical trials), clinical data management, clinical science, and various data science groups

Required core competencies:

* Understanding of the concept of continuous and categorical data; familiarity with clinical data a plus
* Intermediate programming ability in R or python
* Able to write and debug code independently
* Graduate-level statistics courses are a plus
* Expert problem solver capable of seeking help when needed
* Excellent communicator and team player; comfortable explaining complex technical topics to non-technical audiences
* Passion for learning and curious about drug development in the biotech industry

Internship Tasks May Include:

* Data analytics: Developing code for processing or exploration of digital health (device) and/or genomic data including aggregation with patient level data
* Data engineering: Exploring efficiencies in data flow from raw to analysis-ready data sets
* Machine learning research: Suggesting and implementing new methodologies in machine learning for data exploration to enable scientific reverse translation
* Innovation: Driving proof of concept work to explore usage of new software or programming language in a component of our work
* Develop R packages: Developing or refactoring code to create reusable R packages for data processing or visualization