Applied Scientist I, AWS Activate

Key Skills

Java Programming, Design of Experiment, Python Programming, SQL, MATLAB Programming, R Programming, SAS, SPSS Statistics

Job Description

Job summary

Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work._

Please visit https://www.amazon.science for more information._

AWS Activates charter is to help startups get started in AWS. Activate wants to help startups be successful in AWS by providing tools, services, and credits to well qualified startups. To maximize our assistance to startups Activate looks to intelligently apply those resources to those who can benefit the most from it. Through modeling, risk and trend analysis, and historical data we look to provide the best experience for our startup customers via key just-in-time (JIT) engagements, and credit issuance. By engaging startups at the right time we will provide them a ladder to an optimized AWS experience. The Activate Organization is looking for an Applied Scientist for it's Data and Intelligence (D&I) team, whose mission is to combine advanced analytics, with investigator insight to help the business identify high potential startups, those startups requiring meaningful timely engagement, those who may be potential churn risks, and identify fraud and abuse.

As a Applied Scientist, you will be responsible for modeling complex problems, discovering insights, and building cutting edge risk algorithms that identify opportunities through statistical models, machine learning, and visualization techniques to improve operational efficiency and reduce fraud.

You will need to collaborate effectively with business and product leaders within Activate and cross-functional teams to build scalable solutions against high organizational standards. The candidate should be able to apply a breadth of tools, data sources, and ML techniques to answer a wide range of high-impact business questions and proactively present new insights in concise and effective manner.

The candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high impact role with goals that directly impacts the bottom line of the business.

Responsibilities:

• Analyze terabytes of data to define and deliver on complex analytical deep dives to unlock insights and build scalable solutions through Data Science/ML to ensure the best experience for AWS Activate Customers
• Build Machine Learning and/or statistical models that evaluate the startup legitimacy and track impact over time
• Ensure data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, and cross-lingual alignment/mapping
• Define and conduct experiments to validate/reject hypotheses, and communicate insights and recommendations to Product and Tech teams
• Develop efficient data querying infrastructure for both offline and online use cases
• Collaborate with cross-functional teams from multidisciplinary science, engineering and business backgrounds to enhance current automation processes
• Learn and understand a broad range of AWS’s data resources and know when, how, and which to use and which not to use.
• Research and implement novel machine learning and statistical approaches
• Maintain technical document and communicate results to diverse audiences with effective writing, visualizations, and presentations

About Us

Inclusive Team Culture

Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance

Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives

Mentorship & Career Growth

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• Masters in quantitative discipline, e.g. Mathematics, Statistics, Operations Research, Artificial Intelligence
• 1+ years’ experience in business analytics, forecasting or business planning with emphasis on analytical modeling, quantitative reasoning and metrics reporting preferably from an internet environment
• 1+ years of hands on experience with statistical software tools: SAS, SPSS, Strata, R, Python or Matlab

PREFERRED QUALIFICATIONS

• Experience in machine learning and statistical techniques such as classification, clustering, regression, statistical inference, collaborative filtering, and natural language processing, experimental design, social networking analysis, feature engineering etc.
• Demonstrate understanding and experience with relational data sets, data warehouses, data mining and data analysis techniques
• A PhD in CS, Machine Learning, Statistics, Operations Research, Artificial Intelligence
• 2+ years of industry experience in predictive modeling and analysis
• Compelling communication and influencing skills and participation in winning the support of management and influence the course of major strategic decisions
• Experience in e-commerce / on-line companies in fraud / risk control functions