Data Scientist

Burbank, CA 91504

Employment Type: Contract Job Category: Data Science Job Number: 19368

Data Scientist
  • Reframe objectives as machine learning tasks that can deliver actionable insights, accurate predictions, and effective optimization. 
  • Implement and execute machine learning with reliability and reproducibility. 
  • Explain how models and systems work to both non-technical and technical stakeholders. 
  • Collaborate with engineering teams to build data-based products and help integrate into the products and operational processes. 
  • Process, cleanse, and verify the integrity of data used for analysis. 
  • Enhance data collection procedures to include information that is relevant for creating better ML models. 
  • Create automated anomaly detection systems and constant tracking of its performance

  • Development of prototype solutions, mathematical models, algorithms, machine learning techniques, and robust analytics to support analytic insights and visualization of complex data sets
  • Provide optimization recommendations that drive KPIs established by product, marketing, operations, PR teams, and others
  • Drive innovation by exploring new experimentation methods and statistical techniques that could sharpen or speed up our product decision-making processes
  • Develop and deploy testing hypotheses and analyze test results, providing the necessary analytical rigor to ensure data quality, consistency, repeatability, and accuracy of insights
  • Desire to participate in an “ Open Source” learning environment where sharing, documenting, teaching, and collaborating with others is the culture

  • BS in Data Science or Computer Science 
  • Minimum 5 years of relevant experience in Data Science. 
  • Experience with ML frameworks such as TensorFlow, SparkMLlib, Apache Mahout, PySpark, Torch, Caffe, H2o, etc 
  • Demonstrated delivery of machine learning techniques in real-time applications. 
  • Expertise in modern statistics/data science/machine learning. 
  • Expertise in a statistical programming language (we use Python and R internally) and data access tools (e.g., SQL). 
  • The candidate must have a sufficient understanding of and practical experience with classic statistical modeling techniques (e.g., logistic regression, CART, K-means clustering) and machine learning algorithms (e.g., gradient boosting, neural networks, random forest, etc.). 
  • Comfort with large, ambiguous streams of data across different formats and entry points; Hands-on experience processing large datasets; hands on experience with cloud environments (e.g., AWS, Snowflake and Big Data technologies (e.g. Hadoop, Spark) 
  • Experience developing high value features; Hands-on experience deploying models in real-time environments 

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