Data Scientist II, AMZL, Central Learning Solutions Science

Description

The Central Learning Solutions (CLS) Science team in Amazon Operations builds state-of-the-art solutions for enhancing leadership and associate development across the organization. We develop technology and mechanisms for building personalized learning experiences based on the profiles of different learners and assess the post-training performance curves for different learner profiles.

As a Data Scientist on the team, you will be driving the data science roadmap for the CLS Science team. You will leverage your knowledge in statistics and econometrics, estimate the causal impact of training interventions, recommend the right interventions for a given learner profile, and measure the post-launch success of these interventions through A/B weblabs. These insights will help in dynamically changing the training content of Learning & Development courses and unlock opportunities to improve both training effectiveness and learner experience.

You will collaborate effectively with internal stakeholders and cross-functional teams for solving business problems, create operational efficiencies, and deliver successfully against high organizational standards.

• Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation.

• Use advanced causal inference methodologies to estimate the learning curves for different learner profiles and the effectiveness of training content.

• Perform statistical analysis and statistical tests including hypothesis testing and A/B testing.

• Implement new statistical, machine learning, or other mathematical methodologies to solve specific business problems.

• Present deep dives and analysis to both technical and non-technical stakeholders, ensure clarity, and influence the strategy of business partners.

Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment.

The benefits that generally apply to regular, full-time employees include:

• Medical, Dental, and Vision Coverage

• Maternity and Parental Leave Options

• Paid Time Off (PTO)

• 401(k) Plan

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you!

At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Central Learning Solutions (CLS) is the centralized technical team that builds Learning & Development products for North America Operations employees. CLS Science teams owns the science components that power the different products on Impact Estimation, Self Serve Analytics, Content Understanding, and Personal learning.

• Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science

• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

• Experience applying theoretical models in an applied environment

• 2+ years of data scientist experience

• Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.

• 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 with statistical methods (e.g., A/B Testing, Regression)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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