Lensa is a career site for job seekers. They are advertising a position at Amazon Robotics on behalf of Amazon. Amazon Robotics focuses on automating fulfillment center operations using robotics and software. They are constantly researching and developing new technologies in areas like autonomous mobile robots, machine learning, and computer vision.
Job Description:
Amazon Robotics is seeking students for a Data Science Co-op internship for 5-6 months. This is a full-time (40 hours per week) role. By applying, you will be considered for spring and fall co-op roles on various Amazon Robotics teams. The project and location depend on the team you are assigned to.
Key responsibilities:
Work on data science projects within Amazon Robotics.
Apply skills in machine learning, statistics, computer vision, and related fields.
Contribute to the development of robotic and automation solutions.
Basic Qualifications:
Currently enrolled in a Masters or PhD program in Data Science, Mathematics, Statistics, Computer Science, Robotics, Engineering, Machine Learning, Computer Vision, Operations Research, Mathematics, Economics, Computer Science or a related quantitative field.
Degree program must be based in the United States with an expected graduation of December 2025 or later.
Must be eligible and available for full-time internship based out of the assigned office location for the whole duration of the internship/co-op.
At least 1 year of relevant academic research or industry experience within relevant science disciplines such as Machine Learning, Applied Statistics, Computer Vision, Optimization, or related.
At least 1 year of experience working as a data scientist or a similar role involving data extraction, analysis, statistical modeling, and communication.
At least 1 year of experience using data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. R, SAS, Matlab, etc.).
Preferred Qualifications:
Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
Excellent communication skills with ability to explain complex technical concepts to non-technical audience, while also efficiently interacting with deeply technical peers.
Understanding of data engineering and business intelligence.
Quantitative and qualitative data analysis experience with demonstrated impact to a business, a track record of creative problem solving, and the desire to create and build new processes.
Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
Experience with Natural language processing (NLP) and working with large language models.
Experience with causal inference modeling.
Experience with A/B testing and data wrangling.
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