BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent
4+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization.
Proficient in NLP techniques, Explainable AI, and ML frameworks.
Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R.
Efficient in SQL, Hive, SparkSQL, etc.
Comfortable working in a Linux environment
Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
Quick learner, adaptable, with the ability to work independently in a fast-paced environment
Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users