Senior Applied Scientist, GTMC Applied Science

Key Skills

Java Programming, Management, Python Programming, SQL, AWS, Deep Learning, Data Modeling, PyTorch, TensorFlow, R Programming, Algorithms, MxNet, Large scale data processing

Job Description

Amazon’s global talent is incredibly complex with unique problems to be solved for each line of business. Global Talent Management and Compensation (GTMC) is centrally responsible for managing and evolving Amazon’s human capital through intelligent talent products and processes.

Our vision in GTMC Science is to use machine learning and science to scalably solve organizational challenges focused on talent movement, talent differentiation, employee-role matching, promotion processes, organizational design and succession planning, diversity and inclusion, and new areas that address the evolving needs of our diverse employee base.

We are looking for an experienced machine learning scientist to work on talent science products that draw from a range of fields such as algorithmic fairness, natural language processing, supervised and unsupervised learning, recommendation systems, machine learning on graphs, reinforcement learning and others on rich and novel datasets. The role has high visibility to senior Amazon business leaders and involves working with other scientists, and partnering with engineering and product teams to integrate these models into production systems.

As a senior applied scientist in GTMC Science, you will have the opportunity to lead exciting problems in one of the most innovative applications of science in the People Experience and Technology space. You will help to solve high impact business problems in an unconventional domain, and be encouraged to patent and publish your contributions. If this kind of work excites you, reach out to us to find out more!

Key job responsibilities

Successful candidates will have experience in prototyping and launching machine learning methods for large scale prediction problems.

A day in the life

We regularly use data to pitch ideas and drive conversations with Amazon’s Senior Vice President of HR and other executives about how to improve existing talent programs to solve organizational problems focused on (but not limited to) talent differentiation, talent movement, employee-role matching, product integration, promotion practices, organization design and succession planning, and diversity and inclusion, or invent new ones that address the evolving needs of our diverse employee base.

About the team

GTMC Science is a growing interdisciplinary science team within GTMC that develops science products and services to facilitate Amazon’s growth and development of talent across all of our businesses and locations around the world. GTMC Applied Science is responsible for building and deploying models for all product science to manage Amazon talent including for fair talent outcomes and internal talent marketplace.

BASIC QUALIFICATIONS

• MS or PhD in Computer Science, Statistics, Math, Electrical Engineering, Economics or related fields
• 5+ years of experience applying statistical/ML models in large-scale production applications
• Skilled in Python, R or similar scripting language
• Basic knowledge of SQL
• Knowledge and practical experience in more than one of the following areas: machine learning, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling
• Demonstrated use of modeling and optimization techniques tailored to meet business needs.
• Track record of solving ambiguous problems and delivering complex software systems to customers
• Excellent technical writing and communication skills and ability to convey scientific concepts to non-technical business audiences
• Highly adaptable, creative, and thrives in a fast-paced work environment

PREFERRED QUALIFICATIONS

• Deep experience in several of the above areas and experience with libraries such as Tensorflow/MxNet/PyTorch
• Track record of publishing in premier journals and conferences
• Knowledge of software engineering best practices for the full software development life cycle (i.e., coding standards, code reviews, source control management, build processes, and testing)
• Familiarity with AWS and knowledge of distributed computing
• Experience with customer or employee sentiment data