Data Scientist II, AMZL Learning Product & Science

- 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 in a large technology company.
- 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 in hypothesis testing, causal inference, experimental design, A/B testing

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
The Learning & Development Science team in Amazon Logistics (AMZL) builds state-of-the-art Artificial Intelligence (AI) solutions for enhancing leadership and associate development within the organization. We develop technology and mechanisms for building personalized learning courses based on the profiles of different learners and asses the post-training performance curves for different learner profiles.

As a Data Scientist on the team, you will be driving the data science/ML roadmap for the AMZL Learning & Development 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.

Key job responsibilities
- 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.

About the team
We serve AMZL’s Learning teams as the strategic thought leader, looking beyond where other teams are focused to drive transformative solutions that leverage technology and processes to improve learning outcomes and drive down the cost to serve.