Amazon Web Services is the largest consumer cloud offering in the world, powering cutting-edge science, rapidly growing startups, and industry leaders.
This team builds the systems and services that ensure that AWS customers can rely on the highest-availability, lowest-latency cloud platform on the planet.
At the scale of Amazon, unique and complex problems are a part of our daily-life. If you are excited about the prospect of solving problems never heard of and use massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models, then we’d love to hear from you!
Our team in Sydney is working on a brand-new, Sydney born and bred, public AWS product. Our success depends on our ability to process data produced by AWS and by our customers, globally, in real-
time across a wide spectrum of problems. The product built by this team solves critical problems for large and small organizations across the world, and lives up to the high Availability and Reliability standards already associated with an AWS product.
Since we are just getting started, you have the unique opportunity to shape the future of how Data Science impacts this space.
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 data sets to find actionable outcomes that improve our customer’s experience.
We are looking for someone who is passionate about data, has deep expertise and experience in processing and manipulating huge amounts of data, both historical and in real-
time. The successful candidate will also have experience working in a development team to translate their models into working software.
In addition, the 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
MS in Machine Learning, Mathematics, Statistics, Computer Science or in another highly quantitative field
8+ years of relevant academic research or industry experience in developing algorithms
Previous experience in a ML or data scientist role and a track record of building ML or DL models
Experience using Python and / or R
Knowledge of SparkML
Experience handling terabyte size datasets
Track record of diving into data to discover hidden patterns
Familiarity with using data visualization tools
Strong verbal / written communication & data presentation skills, including an ability to effectively communicate with both business and technical teams
PhD in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.)
4+ years of industry experience in predictive modeling and analysis
Good skills with programming languages, such as Java, C / C++ or others
Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
Consulting experience and track record of helping customers with their AI needs
Publications or presentation in recognized Machine Learning, Deep Learning and Data Mining journals / conferences
Experience with AWS technologies like Redshift, S3, EC2, Data Pipeline, & EMR
Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer’s organization
Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment