2255 N Ontario Avenue
Job Category: Data Science
Job Number: 19368
- 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