Data Scientist Intern
Skills Preferred
Job Description
Amazon internships require full-time commitment. During the course of internship, interns should not have any conflicts including but not limited to academic projects, classes or other internships/employment. Any exam related details must be shared with the hiring manager to plan for absence during those days. Specific team norms around working hours will be communicated by the hiring/ reporting manager at the time of commencement of internship. Candidates receiving internship confirmation shall be required to submit declaration of their availability to complete the entire duration of internship, duly signed by a competent authority at their University. Internship commencement will be subject to successful submission of the declaration.
Responsibilities
Use data analyses and statistical techniques to develop solutions to improve customer experience and to guide business decision-making. Identify predictors and causes of business-related problems and implement novel approaches related to forecasting and prediction. Identify, develop, manage, and execute analyses to uncover areas of opportunity and present written business recommendations. Collaborate with multiple teams as a leader of quantitative analysis and develop solutions that utilize the highest standards of analytical rigour and data integrity. Analyze and solve business problems at their root.
Job Requirements
Basic Qualifications: Record of delivering large analytical solutions with business impact. Experience on R/SAS/Matlab and SQL. Excellent Microsoft Office skills, including a strong working knowledge of Excel. Problem-solving ability and passion for big data. Excellent communication and data presentation skills. Fluent in written and spoken English. Preferred Qualifications: Masters or equivalent advanced degree in Computer Science, Computer Engineering, Statistics, Mathematics or related technical discipline. Hands-on experience and project-based learning in computer science, engineering or mathematics is preferred. Academic experience in manipulating/transforming data, model selection, model training, cross-validation and deployment at scale. Academic or Project Experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and PyTorch. Academic Experience with Big Data platforms like Apache Spark and Hadoop. Familiarity with data processing with Python, R & SQL. Familiarity with AWS services related to AI/ML highly desirable, particularly Amazon EMR, AWS Lambda, SageMaker, Machine Learning, IoT, Amazon DynamoDB, Amazon S3, Amazon EC2 Container Service, Green Grass etc.
Salary
