Sr. Data Scientist, Workplace Health and Safety

Key Skills

Java Programming, Analytical and Problem solving, Python Programming, SQL, Agile methodologies, Big Data Technology, Machine learning techniques, Data science techniques, A/B Testing, Data Modeling, Natural Language Processing (NLP), Statistics, Amazon RedShift, Amazon Elastic Compute Cloud-EC2, Amazon Simple Storage Service (S3), Data pipelines, ETL frameworks, Mathematics, Large data sets, Data Analysis, Data Warehousing

Job Description

Amazon’s Workplace Health and Safety organization is changing the way we think about our workplace, so that we can keep our associates healthy and safe. We have just started scratching the surface in terms of possibilities, and are exploring everything from using wearable devices to improve health, to analyzing data from all Amazon buildings and warehouse management systems to predict safety-related events. Our team is reinventing the way Amazon anticipates, assesses, and manages workplace hazards and risks.

We are looking for an experienced data scientist with superior analytical skills. This position is uniquely positioned in the team as we have a growing need for looking into new datasets, prioritize opportunities using data driven insights and solution these opportunities using different Machine Learning and AI techniques.

To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and robust machine learning models from multiple data sources, and deliver results that meet high standards of data quality, security, and privacy.

Key job responsibilities

• Lead Data Science solutions from beginning to end.
• Deliver with independence on challenging large-scale problems with complexity and ambiguity.
• Write code (Python, R, Scala, SQL, etc.) to obtain, manipulate, and analyze data.
• Build Machine Learning and statistical models to solve specific business problems.
• Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.
• Analyze historical data to identify trends and support optimal decision making.
• Apply statistical and machine learning knowledge to specific business problems and data.
• Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.
• Give anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.
• Build decision-making models and propose effective solutions for the business problems you define.
• Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.

BASIC QUALIFICATIONS

• Master’s degree in Statistics, Engineering, Computer Science, Mathematics, or a related field
• 5+ years of working experience as a Data Scientist
• Ability to structure a problem, gather supporting data using big-data technology (AWS tools, SQL, Python) and large-scale data warehousing, and write strong narratives to convey findings.
• 5+ years of diverse experience in data science methodologies (statistics/ML modeling, A/B test experimentation, statistical analysis, causal inference, etc.)
• Knowledge on unstructured text data analysis/ sentiment analysis/ model technologies
• Experience on data visualization/dashboard by Tableau
• Strong communication and data presentation skills and problem solving ability

PREFERRED QUALIFICATIONS

• A PhD in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)
• 8+ years of experience in a ML or Data Scientist role with a large technology company
• Fluency in a scripting (e.g. Python) and SQL coding
• Experienced in quantitative modeling/ NLP text analysis and time series forecasting
• Focused knowledge/experience in at least one data science methodology (statistics/ML modeling, experimentation, statistical analysis, causal inference, probabilities, etc.)
• Experienced in data modeling, ETL development, and Data Warehousing
• Experience working with large-scale data warehousing and analytics projects, including using AWS technologies – Redshift, S3, EC2, Data-pipeline and other big data technologies.