Data Science Intern, Applied Machine Learning at Tempus

| Chicago
!Sorry, this job was removed at 12:11 p.m. (CST) on Thursday, May 16, 2019

With the advent of genomic sequencing, we can finally decode 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 this 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're on a mission to connect an entire ecosystem to redefine how patient data is used in clinical settings. Our Data Science team is passionate and focused on applying state of the art machine learning techniques to the processing and analysis of vast amounts of clinical, molecular, and imaging data.

What You’ll Do

  • Collaborate with product, science, engineering, and business development teams to build the most advanced data platform in precision medicine
  • Design and prototype novel data visualization and analysis tools and algorithms
  • Wrangle and analyze large diverse sparse datasets, extract insights, and drive further research opportunities
  • Interrogate analytical results for robustness, validity, and out of sample stability
  • Document, summarize, and present your findings to a group of peers and stakeholders

Required Qualifications:

  • Degree in computer science, software engineering, statistics, machine learning, bioinformatics or related technical field
  • Project experience building and validating predictive models on structured or unstructured data
  • Proficient in Python, and SQL
  • Experience with the following: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks
  • Experience with supervised and unsupervised machine learning algorithms, and ensemble methods, such as: K-Means, PCA, Regression, Neural Networks, Decision Trees, Gradient Boosting
  • Experience working in a Linux / Mac environment

Preferred Qualifications

  • 2+ years full time employment experience building and validating predictive models on structured or unstructured data
  • Kaggle.com competitions and/or kernels track record
  • Experience working with clinical and/or genomic data
  • Experience with AWS architecture
  • Experience with: Git, matplotlib, seaborn, HTML5, CSS3, JavaScript, D3, Plot.ly, Flask, Dask
  • Experience in agile environments and comfort with quick iterations
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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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