Senior Data Scientist, Employee Experience

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Please note this role can be based in various locations: Seattle (WA), Atlanta (GA), Houston (TX) , Austin (TX), and Arlington (VA).

Are you passionate about making real-world impact on the lives of over one million Amazon employees? Do you want to use science and data-driven methods to build Earth’s best employer and safest place to work? If your answers to these questions are “yes”, then come join the Employee Experience (EX) science team within People Experience and Technology (PXT) at Amazon. Through science, research, and technology, the team’s mission is to improve the employee experience for all Amazonians.

As a Senior Data Scientist on the EX science team, you will lead key science initiatives and deliver insights that enable Amazon to create a great employee experience. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from datasets, create intuitive data visualizations, and build scalable machine learning models. You will work closely with other scientists (data, research, and applied scientists), business intelligence engineers, and product managers to obtain relevant datasets and prototype predictive analytic models. You will team up with data engineers and software development engineers to implement data pipeline to productionize your models and review key results with business leaders and stakeholders.

We are looking for someone who can work with ambiguous business problems and navigate complex and dynamic business environments. In this role, you will develop, execute, and deliver on detailed technical roadmap that influences across organizations. The successful candidate is able to make complex trade-offs between technical and business requirements and has experience striking a balance between scientific validity and business practicality in their work. They also have the ability to effectively communicate technical results to a business audience in order to influence key leadership decisions.

Key job responsibilities

• Create innovative, sophisticated analytic models to address critical issues but also meet key business criteria (cost/risk/business impact) and key technical criteria (reliability/validity/predictability).
• Apply machine learning techniques and statistical methods to automatically identify trends, patterns and frictions in the employee experience.
• Interview stakeholders to incorporate business requirements and inputs from research, science, product, and engineering partners and translate them into concrete requirements for data science projects.
• Define and conduct experiments and communicate insights and recommendations to product, engineering, and business teams.
• Work with data engineers and software development engineers to deploy models and experiments to production.
• A clear passion for making a positive impact on employees' lives through your work.
• Identify and advocate for technical options related to machine learning, data mining, and other statistical approaches.
• Identify and recommend opportunities to automate systems, tools, and processes.
• Ability to work in a highly collaborative environment with peers that have a range of technical aptitudes.

BASIC QUALIFICATIONS

• Master's Degree in a quantitative field (such as Computer Science, Economics, Mathematics, Statistics, Engineering, Operations Research, Machine Learning, or related field).
• 5+ years experience working as a Data Scientist.
• 5+ years experience programming in Java, C++, or other programming language, as well as experience using common data scientist software development and statistical analysis tools (e.g., Python, R, Scikit-learn).
• Experience writing and optimizing SQL queries in a business environment with large-scale, complex datasets.
• Experience applying various machine learning techniques and a deep understanding of the key parameters that affect their performance.
• Highly proficient at statistical analysis, model development, model validation, and model implementation for large-scale applications.

PREFERRED QUALIFICATIONS

• PhD in a quantitative field (such as Computer Science, Economics, Mathematics, Statistics, Engineering, Operations Research, Machine Learning, or related field).
• 10+ years industry experience working as a Data Scientist with experience in predictive modeling, data analysis, and a track record of building Machine Learning and/or Deep Learning models.
• 4+ years of experience with experiment design involving algorithm development, machine learning, data mining, and use of other statistical approaches.
• Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relation.
• Experience with machine learning software systems, big data software systems, data mining, and modern methods for parallelized processing of large, distributed datasets (e.g. Spark, Hadoop, Map-reduce).
• History of putting ML models into production.
• Experience using an object-oriented language to write production-ready code.
• Experience using AWS products (Redshift, Sagemaker, Athena, S3, EC2, QuickSight) and with development on the AWS platform.
• Knowledge of professional software development life cycle, including coding standards, code reviews, source control management, build processes, testing and operations.
• Excellent ability to convey complex mathematical and scientific concepts to business stakeholders.