Computational Biology Internship (Summer 2019) at Tempus

| Chicago
!Sorry, this job was removed at 1:06 p.m. (CST) on Wednesday, July 17, 2019

Passionate about making a difference in the world of cancer genomics?

Tempus is on a mission to connect an entire ecosystem to redefine how genomic data is used in cancer clinical settings. With the advent of genomic sequencing, we can finally measure and process our genetic makeup. We now have more data than ever before but providers don't have the infrastructure or expertise to make sense of said data. Here at Tempus, we believe the greatest promise for the detection and treatment of cancer lies in the deep understanding of molecular activity for disease initiation, progression, and efficacious treatment based on the discovery of unique biomarkers.

We are seeking an independent and motivated Computational Biologist Intern to work with our Computational Immunology group. This individual will work in an interdisciplinary team to study the tumor-immune microenvironment using unique and growing collections of genomic data coupled with clinical data. The successful candidate will work in a team, carry out data analysis, and apply best-in-class algorithms - or develop new algorithms - that directly address important biological and clinical questions.

What You’ll Do

  • Design, develop and execute computational research projects of high complexity.
  • Evaluate new emerging technologies in relevant fields.
  • Communicate highly technical results and methods clearly.
  • Interact cross-functionally with a wide variety of people and teams.

Required Qualifications:

  • Working towards a master’s or higher degree in a quantitative discipline (e.g. statistics, bioinformatics, computational biology, computer science, applied mathematics, applied physics or similar). Alternately, working towards PhD in molecular biology, immunology, etc. combined with a very strong record of high-throughput data analysis, or equivalent practical experience.
  • Fluent with R, Python, or similar.
  • Previous experience extracting and cleaning large data sets
  • Interest in immunology and immunotherapy

Preferred Qualifications:

  • Prior experience working in diagnostic technologies, human immunology, cancer biology, infectious disease, and/or high-throughput screening.
  • Significant quantitative training in probability and statistics.
  • Demonstrated willingness to both teach others and learn new techniques.
  • Familiarity with TCGA, GTEx, IMGT, Immgen databases
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Technology we use

  • Engineering
  • Product
  • Sales & Marketing
    • JavascriptLanguages
    • PythonLanguages
    • SqlLanguages
    • AWSLanguages
    • ReactLibraries
    • ReduxLibraries
    • Node.jsFrameworks
    • Microsoft SQL ServerDatabases
    • MySQLDatabases
    • PostgreSQLDatabases
    • SQLiteDatabases
    • tableuAnalytics
    • JIRAManagement
    • SalesforceCRM

Location

600 WEST CHICAGO AVENUE, Chicago, IL 60654

An Insider's view of Tempus

What makes someone successful on your team?

We value creativity in ideation, autonomy in execution, and a healthy tendency to always question the status quo.

Ash

Lead Data Scientist

What are Tempus Perks + Benefits

Culture
Volunteer in local community
Friends outside of work
Eat lunch together
Daily stand up
Open door policy
Team owned deliverables
Team based strategic planning
Group brainstorming sessions
Pair programming
Open office floor plan
Health Insurance & Wellness Benefits
Flexible Spending Account (FSA)
Dental Benefits
Vision Benefits
Health Insurance Benefits
Life Insurance
Wellness Programs
Onsite Gym
Retirement & Stock Options Benefits
401(K)
Company Equity
Child Care & Parental Leave Benefits
Generous Parental Leave
Vacation & Time Off Benefits
Unlimited Vacation Policy
Generous PTO
Paid Volunteer Time
Paid Holidays
Perks & Discounts
Beer on Tap
Casual Dress
Commuter Benefits
Company Outings
Stocked Kitchen
Some Meals Provided
Happy Hours
Relocation Assistance
Fitness Subsidies
Professional Development Benefits
Diversity Program
Lunch and learns
Promote from within
Time allotted for learning
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