Data Scientist in Alpharetta, GA at Scientific Games

Date Posted: 4/16/2018

Job Snapshot

  • Employee Type:
    Full-Time
  • Job Type:
    Science
  • Experience:
    Not Specified
  • Date Posted:
    4/16/2018
  • Job ID:
    IRC6731

Job Description



Job Description:

The Data Scientist offers expertise based on the application of statistical knowledge and predictive modeling. An in-depth understanding of data manipulation, database management systems, statistics, and machine learning is required. The candidate should be comfortable with the model building process (data acquisition, data munging, algorithm selection, hyper parameter tuning, model performance testing, and model implementation). It is crucial to possess deep analytical skills, strong out-of-the-box thinking, and a curious mind.

Key Responsibilities:

• Develop both descriptive and predictive models to help solve real-world industry problems
• Using the results of analysis and modeling, the Data Scientist must effectively communicate impactful business recommendations on a regular basis
• Apply machine learning and data mining techniques to identify growth opportunities
• Build solutions for but not limited to: customer segmentation and targeting, propensity modeling, churn modeling, lifetime value estimation, forecasting, recommendation systems, modeling response to incentives, and price optimization
• Identify and validate industry Key Performance Indicators (KPIs), metrics, and trends
• Transform and manipulate data in preparation for analysis
• Provide testing techniques and methodologies in order to assess the impact and effectiveness of business initiatives
• Keep up-to-date on relevant tools and algorithms

Required Skills and Experience:

• Master’s degree in a field with significant quantitative training (e.g. applied statistics, mathematics, economics, finance, computer science, engineering)
• Previous experience with retail CPG analytics is a plus
• Fluency in R and/or Python
• 2+ years of experience in supervised and unsupervised machine learning methods including but not limited to clustering techniques (e.g. k-means, DBSCAN, spectral clustering), tree-based ensemble classifiers (random forests, gradient-boosted trees), support vector machines, and neural networks
• Familiarity with: general linear modeling, simulation, feature engineering and selection, hyperparameter tuning, cross-validation, data smoothing methods, ARIMA models, Box-Jenkins methodology, multivariate time series analysis
• Knowledge of SQL with the ability to independently write queries in order to extract necessary data
• Organized and capable of independently managing complex analytical projects from start to finish
• Ability to independently structure analyses and communicate findings to a non-technical audience
• Experience with Excel VBA/macros, Tableau, and/or Alteryx is a plus

Job Requirements




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