Senior Data Scientist, Machine Learning
- Flexible / Remote
Coursera is a leading online learning platform for higher education, where 76 million learners from around the world come to learn skills of the future. More than 200 of the world’s top universities and industry educators partner with Coursera to offer courses, Specializations, certificates, and degree programs. Thousands of companies trust the company’s enterprise platform Coursera for Business to transform their talent. Coursera for Government equips government employees and citizens with in-demand skills to build a competitive workforce. Coursera for Campus empowers any university to offer high-quality, job-relevant online education to students, alumni, faculty, and staff. Coursera is backed by leading investors that include Kleiner Perkins, New Enterprise Associates, Learn Capital, and SEEK Group.
At Coursera, our Data Science team is helping to build the future of education through data-powered products and data-driven decisions. In Machine Learning, we define, develop, and launch the models and algorithms that power content discovery, personalized learning, and machine-assisted teaching and grading. In Decision Science, we drive product and business strategy through measurement, experimentation, and causal inference. We believe the next generation of teaching and learning should be personalized, accessible, and efficient. With our scale, data, technology, and talent, Coursera and its Data Science team are positioned to make that vision a reality.
We are looking for a creative, collaborative, and computationally strong Senior Data Scientist, Machine Learning to help deliver high-quality and high-impact data products to production through leading the ML frameworks design and enhancement. In addition to the ML architecture, this role will also help build out Coursera’s Skills Graph and its product applications. Our Skills Graph is a foundational data asset powered by NLP and crowdsourcing that maps a robust library of skills to the content that teaches them, the careers that require them, and the learners who have or want to learn them. Product applications of Skills Graph range from skills-based search and recommendations to real-time learner skill tracking. Our ideal candidate has end-to-end data product experience, including identification of user-facing machine learning opportunities, collaboration with cross-functional product teams, and in-production deployment to bring products to life. It is also crucial that a candidate is impact-driven and shares our passion for education.
Check out life at Coursera on The Muse!
- Designing and enhancing the existing end-to-end architecture for AI solutions using ML tools (e.g., AWS Sagemaker, MLFlow)
- Bringing new technologies and frameworks to our ML team and support onboarding of ML data scientists to platforms
- Collaborating with platform engineering to help guide and design solutions for new machine learning services, tools and approaches for scalable ML solutions
- Ideate, prototype, and productionize the machine learning algorithms that power Coursera’s learning experience, especially Skill Graph and its key applications (e.g., learner skill tracking, skill diagnostics, skill-based recommendations)
- Design, implement and deploy end-to-end machine learning / deep learning pipelines and models with cloud services (e.g., AWS Sagemaker)
- Partner with product to identify and articulate opportunities, drive data product adoption, and build efficient and scalable insights and data products
- Distill insights from complex data and/or data product results; communicate findings clearly to both technical and non-technical audiences
- 3+ years of industry experience on implementing and launching data science pipelines and applications at scale using a general programming language (e.g, Python, Java, Scala)
- 1+ year of experience hosting and deploying ML solutions (e.g., for training, tuning, and inferences)
- Experience with software development tools like Git, CI/CD, Docker, etc.
- Experience in machine learning frameworks like TensorFlow, PyTorch, Scikit Learn, etc.
- 2+ years of work experience in deployment and scaling of Machine Learning and Deep Learning algorithms on AWS cloud services (Sagemaker, Lambda, Cloudwatch, etc.)
- Experience with upskilling teammates with new ML technologies and infrastructures
- Experience with large-scale distributed databases (e.g., Spark)
- Experience with data orchestration tools (e.g., Airflow) and streaming systems
- Experience with online controlled experimentation
- MS or above
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