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Risk Data Scientist

Job description:

    • Understanding and experience with database systems.
    • Understanding and experience with machine learning algorithms.
    • Perform feature analysis.
    • Implement reinforcement learning for optimal sequential decision-making under uncertainty, on real-world problems in cybersecurity, finance, telco, and automotive applications.
    • Create AI-based malware, AML, KYC classifiers that are robust to adversarial evasion attacks and scalable to multiple players.
    • Build utility code and handle miscellaneous support tasks.
    • Conduct data preprocessing, transformation, and perform statistical analysis.
    • Documenting software projects and maintaining project documentation.
    • Working in a team environment as well as working alone.

Qualifications

    • Experience with Big Data, artificial intelligence, natural language processing, machine learning and/or deep learning.
    • Experience with one or more deep learning libraries such as TensorFlow, Keras, Caffe, FLUX, or Theano, and experience with one or more deep reinforcement learning libraries such as rllab, keras-rl, or OpenAI Gym.
    • Python programming skills with two (2) years or more of Python experience.
    • Good verbal and written communication skills.
    • Knowledge of professional software engineering practices and best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
    • Ph. D. in operations research, applied statistics, data mining, machine learning, physics or a related quantitative discipline.

Tools we use

    • Confluence
    • JIRA
    • Spark
    • Azure
    • Python
    • Keras
    • Scikit-learn
    • Bit bucket
    • Jupyter Notebook
    • Scala
    • MonetdB
    • OrientDB

Nice to haves

    • Experience in some subset of the following: Java, R, Python, SQL, Scala, Spark.
    • Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, supervised learning, recommendation and optimization algorithms.


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