Applied Scientist Ii, Pbi Selection Guidance - San Diego, United States - Amazon Services LLC

Amazon Services LLC
Amazon Services LLC
Verified Company
San Diego, United States

3 weeks ago

Mark Lane

Posted by:

Mark Lane

beBee recruiter
Description
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

  • Experience in patents or publications at toptier peerreviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, highperformance computing


Join our Private Brand Intelligence (PBI) organization in building ML/AI driven solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon.


PBI applies Generative AI, Machine Learning, Statistics, and Economics to derive actionable insights about the complex economy of Amazon's retail business.

We also develop ML/Econ models and algorithms to drive strategic business decisions and improve operations.

We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using cutting-edge technology, to solve some of the toughest business problems at Amazon.


We are a Day 1 team, with a charter to be disruptive through the use of the cutting-edge techniques and algorithms.

You will start on green field projects working with engineers to bring our models to life as well as data products that other teams can benefit from.

We are an inclusive team, and are looking for Applied Scientists that aren't averse to learning and building and or optimizing ML models alongside our engineers, product managers and business partners.


As an Applied Scientist on the team, you will drive improvements to our business, collaborating with scientists, economists, engineers and highly-engaged stakeholders to deliver actionable insights continuously.


We're truly an agile shop:

we work closely with users, deliver features with high frequency, can pivot on a dime when needed, and are passionate about solving customer pain points.

We are looking for applied science leaders who share our vision for continuously improving the customer experience, who are motivated by challenging business problems and who love thinking big.

Key job responsibilities

  • You will take the lead on large projects that span multiple teams. The problems you solve will be ambiguous, requiring both technical and domain expertise. You will deliver significant benefits to business.
  • You need to understand challenges in your teams' business area, the applicability of relevant machine learning disciplines, and interactions among systems. You will influence your team's technical and business strategy by making insightful contributions to team priorities and approach.
  • You will make solutions simpler. You will optimize connected systems using their dynamics. You will improve the consistency and integration between your team's solutions and the work being done by related teams.
  • You will improve the work done by others, either via a collaborative effort or by increasing their scientific knowledge, using specialized tools or advanced techniques. You will lead and actively participate in scientific reviews for your team and others.
  • You are able to communicate your ideas effectively to achieve the right outcome for your team and customer including when to make appropriate tradeoffs. You harmonize discordant views and lead the resolution of contentious issues.
  • You actively participate in the hiring process and improve the skills and knowledge of others via mentoring.
San Diego, CA, USA | Seattle, WA, USA

  • Experience using Unix/Linux
  • Experience in professional software development
  • Knowledge of AWS tech stack (e.g., AWS SageMaker, Redshift, S3, EC2, Glue, EMR);
  • Handson experience with designing and analyzing large scale A/B tests and working knowledge of causal inference models;
  • Experience developing end to end production grade ML pipeline
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