Data Scientist II, Inflow and Capacity Optimization, Operations Risk Compliance
DESCRIPTION
At Amazon, we're revolutionizing the way compliance is done with a vision of achieving full automation at zero risk through risk-aware machine learning and optimization solutions. Our ORC Science team plays a key role in driving this transformation by developing state-of-the-art ML solutions for classification and we are constantly innovating to improve the efficiency of our global classification operations, where scale, complexity, and speed are at the forefront of everything we do.
We are seeking a talented and driven Data Scientist to join our team to work on forecasting and optimization solutions to optimize inflow and capacity planning for classification.
In this role, you will have the opportunity to work on complex and impactful challenges, blending machine learning, optimization, and data science to solve problems at scale. Your work will directly improve the efficiency, automation and quality of Amazon’s classification operations, ultimately driving better outcomes for customers and associates worldwide.
Key job responsibilities
- Build state-of-the art, robust and scalable (Probabilistic) Forecasting and (Stochastic) Optimization solutions for optimal and risk-aware inflow and capacity planning in compliance
- Design and engineer algorithms using Cloud-based state-of-the art software development techniques
- Think multiple steps ahead and develop for long term solutions while continuously delivering incremental improvements
- Prototype fast, ensure early adoption via pilots, integrate feedback into the models, and iterate
- Conceptualize and operationalize (i.e. deliver) your science solutions by closely partnering with internal customers, understand their needs/blockers and influence their roadmap
- Lead complex analysis and clearly communicate results and recommendations to leadership
- Act as an active member of the science community by researching, applying and publishing internally/externally the latest OR/ML techniques from both academia and industry
BASIC QUALIFICATIONS
- Master's degree in operations research, applied mathematics, theoretical computer science, or equivalent
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
- Experience diving into data to discover hidden patterns and of conducting error/deviation analysis
- 2+ years experience with Time-Series Probabilistic Forecasting models and Stochastic Optimization algorithms (e.g. Stochastic Linear Programming, Stochastic Dynamic Programming)
- Sharp analytical abilities, excellent written and verbal communication skills
- Ability to handle ambiguity and fast-paced environment
PREFERRED QUALIFICATIONS
- PhD
- Experience in a ML or data scientist role with a large technology company
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
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