Data Scientist Intern - Spring 2018, Payformance Solutions

Location US-IL-Chicago
# Positions


About Us: 

We are Chicago based team. Our team writes beautiful & functional code that is easily reproducible and testable. We encourage open source contributions, especially with the technologies we use. We don’t release on Fridays.

Our team is currently looking for a data science intern to join our team in March or April.  This position would work an average of 10-15 hours per work to help support our data analytics needs.  As a data science intern, you will utilize a diverse array of technologies and tools, as needed, to deliver insights (e.g., Python, Juypter). You will be a contributor to the team, tasked with developing analytics models and algorithm solutions.


About You: 

We want developers that are serious about their work but don’t take themselves too seriously. You are flexible, can quickly pick up new technologies, and are a DIYer with a GSD mindset.


Minimum Experience:

  • Background writing in Python, Scala
  • Experience with SQL & noSQL data stores
  • Experience with data cleansing
  • Experience with modern ML libraries such as Tensorflow, Keras, SciKit Learn, etc

Skills (must have some or all):

  • Previous published academic research in artificial intelligence, machine learning, or operations research
  • Familiarity with any or all of the following: Dirichlet Latent Process, Lasso,SpAM/Ridge regularization, Ensemble methods, Time series analysis (GARCH, auto-regressive or moving average, etc.), Bayesian inference, Random forest and decision tree models, Basic Data Analysis (Histograms, Scatterplots), Aggregation, Split-Apply-Combine/Map-Reduce, Pivot Tables
  • Statistics: Distributions, Bayes' Theorem, t-Testing, ANOVA
  • Experience with linear regression analysis
  • A grasp of non- and paramtric classification techniques: discriminant analysis, ensemble methods, neural networks
  • Experience with dimensionality reduction: Principle Components Analysis (PCA)/Online PCA, Singular Value Decomposition (SVD)
  • Unsupervised Learning: Hierarchical (Agglomarative/Divisive) Clustering, k-Means, Expectation Maximization, Mixture of Gaussians
  • Experience working with task schedulers (e.g., Aurora, Marathon, Airflow, etc.)
  • Experience with distributed computing / concurrent data pipelining

Education: A minimum Bachelor’s Degree in Computer Science, Engineering or any equivalent education or experience. Currently pursuing or recently completed a advanced degree in Computer Science, Engineering, or Analytics.

At Payformance, we don’t just accept difference - we celebrate, support, and thrive on it for the benefit of our employees, our clients, and our community. Payformance Solutions is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to, among other things, race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, status as a protected veteran, or disability.


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