The Challenge:

Are you excited at the prospect of unlocking the secrets held by a data set? Are you fascinated by the possibilities presented by machine learning and artificial intelligence advances? In an increasingly connected world, massive amounts of structured and unstructured data open up new opportunities. As a data scientist, you can turn these complex data sets into useful information to solve global challenges. Across private and public sectors — from fraud detection, to cancer research, to national intelligence — you know the answers are in the data.

You’ll design and implement machine learning solutions for complex tasks on large data sets, including extracting insights from multiple disparate data sources and types, including cyber, language, and vision. You’ll perform research in machine learning, including adversarial machine learning, algorithmic fairness, and model interpretability. You’ll write journal articles, present at academic conferences, and produce whitepapers and briefings for both technical and non-technical audiences. You’ll serve as the client interface and maintain responsibility across the entire life cycle, including requirements gathering and analysis, process and systems definition, data analysis, presentation of analysis to clients in a format they can digest, and development of algorithm driven products and solutions.

You Have:

• Experience with programming in Python
• Experience with machine learning, including Bayesian machine learning methods
• Knowledge of reinforcement learning algorithms for decision making under uncertainty
• Knowledge of mathematics and statistics, including coursework in the theory of probability, statistical inference, algorithms, linear algebra, and calculus
• Ability to derive a variational inference procedure mathematically for a novel model and implement the inference procedure in a framework, including PyTorch or Numpy
• Ability to obtain a security clearance
• BA or BS degree

Nice If You Have:

• Experience with application areas of machine learning, including computer vision, natural language processing, and learning on graphs
• Experience with Bayesian deep learning and Gaussian processes
• Experience with building complex data pipelines
• Experience with using GPUs for machine learning using frameworks, including PyTorch or TensorFlow
• Knowledge of cloud systems, including AWS, Azure, or GCP
• Ability to communicate results to both technical and non-technical audiences effectively
• Ability to work independently on complex tasks

Clearance:

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information.

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