• Senior Applied Scientist

    Company/Location (search) AU-NSW-Sydney
    Posted Date 4 weeks ago(5/1/2018 4:37 PM)
    Job ID
    # Positions
    Position Category for Posting
    Machine Learning Science
  • Job Description

    Amazon Web Services is the largest consumer cloud offering in the world, powering cutting-edge science, rapidly growing startups, and industry leaders. The AWS Reliability Engineering team builds the systems and services that ensure that AWS customers can rely on the highest-availability, lowest-latency cloud platform on the planet. If you are looking for a position where you can wrangle data to directly impact the infrastructure relied upon by millions of people very day, then we'd love to hear from you.

    Our success depends on our ability to process huge amounts of data to determine the health of AWS across its various services and products. We build highly available services that use a variety of technologies, including high speed data processing, Machine Learning and highly customizable automation engines, to detect and mitigate operational issues before they impact customers. A successful candidate will be passionate about data, having the ability to manipulate large amounts of data in order to build models that can be used to build the next generation of reliability detection and response systems.

    In this position, you will research and develop innovative machine learning based approaches to predict the near-term future. You will have ownership over our data strategy, working in one of the world's most diverse and complex data environments, bringing together loosely structured datasets to find actionable outcomes that improve our customer's experience. You should have deep expertise in processing and manipulating huge amounts of data, both historical and in real-time as well as experience working as part of a dev team to productize your findings.

    The right candidate will possess excellent business and communication skills, be able to work with engineers as well as business owners to formulate our data strategy and drive its execution across the globe.

    Job Responsibilities:

    • Develop quantitative models across multiple reliability data slices for use in Machine Learning and other systems
    • Develop hypotheses, design experiments, collaborate with engineering team to implement live tests and evaluate their performance
    • Answer complex business questions by using appropriate statistical techniques on available data or designing and running experiments to gather data
    • Manage critical processes that are central to the delivery of accurate results (e.g. data pipelines, etc.)
    • Communicate findings to managers and engineers, often through succinct written summaries of findings and code samples

    Basic Qualifications

    • MS in Machine Learning, Mathematics, Statistics, Computer Science or in another highly quantitative field
    • 5+ years of relevant academic research or industry experience in developing algorithms
    • Ample experience with languages used for querying (e.g. Hive/Pig/SQL), preprocessing (e.g. Shell Scripting/Python), and statistical analysis (e.g. R)
    • Experience working across different types of databases (Relational, NoSQL, Graph etc)
    • Proven achievements resulting from data analysis and ability to succeed in both collaborative and independent work environments
    • Strong verbal/written communication & data presentation skills, including an ability to effectively communicate with both business and technical teams
    • Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources

    Preferred Qualifications

    • PhD in Machine Learning, Mathematics, Statistics, Computer Science or in another highly quantitative field
    • Experience with Java and Spark
    • Experience with Stata and Mosel
    • Experience with AWS services such as EMR, EC2, S3, and Redshift
    • Experience in Predictive Analytics
    • Expertise in machine learning methods including Time series analysis, State-space models, Mixed-effect models, Longitudinal data analysis, Hierarchical Bayes; and Learning techniques such as Decision Trees, Boosting, Random Forests, Deep Learning, Neural Networks
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