Crowe

  • Data Scientist

    Location US-IL-Chicago
    ID
    18059
    Firm Services
    University Entry Level - Consulting
    Type
    Regular Full-Time
    Anticipated Start Date
    6/3/2019
  • Company Overview

    Your Journey at Crowe Starts Here:

    At Crowe, you have the opportunity to deliver innovative solutions to today’s complex business issues. Crowe’s accounting, consulting, and technology personnel are widely recognized for their in-depth expertise and understanding of sophisticated process frameworks and enabling technologies, along with their commitment to delivering measurable results that help clients build business value. Our focus on emerging technology solutions along with our commitment to internal career growth and exceptional client value has resulted in a firm that is routinely recognized as a “Best Place to Work.” We are 75 years strong and still growing. Come grow with us! 

    Position Summary

    Given substantial growth and impact at Crowe, the machine learning team has formed into an official unit, Advanced Data Science (ADS), which is responsible for all machine learning and artificial intelligence throughout the firm. This team works on a wide variety of projects utilizing numerous areas of machine learning, including: supervised regression/classification, unsupervised learning, anomaly detection, NLP, reinforcement learning, deep learning, forecasting, etc.

     

    Leverage large sets of structured and unstructured data to develop tactical and strategic insights. Collaborate with analytic and data teams to set objectives, approaches, and work plans. Research and evaluate new analytical methodologies, approaches, and solutions. Develop and validate statistical forecasting models and tools. Interpret and communicate analytic results to analytical and non-analytical business partners and executive decision makers.

    Qualifications

    Education

    • We are seeking candidates completing an advanced degree (Masters at a minimum) in a quantitative field in one or more of the following:
      • Statistics
      • Computer Science
      • Data Science
      • Engineering
      • Physics
      • Mathematics
    • Computational Social Science
      • [or similar academic pedigree]
    • A minimum 3.0 GPA (major and cumulative) is required; 3.2 GPA (major and cumulative) is preferred. 

    Required Skills

    •  Modeling experience (at least 2 of the following)
      • Forecasting (ARIMA, ARCH, GARCH)
      • Supervised Classification/Regression
      • Anomaly Detection
      • Dimension Reduction
      • Clustering Techniques
      • Hypothesis Testing
      • Neural Networks (Feed-forward, CNN, RNN, etc.)
      • Parallel Processing (CPU & GPU)
      • Cluster and cloud computing: Spark, Mesos, Azure ML, etc.
    • Experience with a statistical package & various programming languages. Your job will require you to code - we write Python and R, so experience in these languages is preferred.
    • Machine learning experience. You enjoy machine learning. You've gone deeper than reading blog posts, ideally having several example machine learning projects you can speak to within industry, academia, or your free time.
    • Capacity for autonomy. You won't have to hit the ground running on day one, but you will have to manage your time between research, model development, and model deployment.
    • Strong communication skills. You can both dive deep in machine learning and explain concepts to a non-technical audience.

    Preferred Skills

    • Business Intelligence experience (Microsoft Power BI / Tableau
    • Experience writing SQL queries and linking to enable read/write to the database.
    • Hackiness. You like to code and have interest in learning new technologies. Using git and Docker as regular parts of your workflow sounds exciting.
    • Curiosity about machine learning.  You want to stay fresh in machine learning. You aren't afraid to take a stab at deep learning papers before they become blog posts (even if you don't have experience in it quite yet).

    Locations:

    • Chicago, IL

     

     

     

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