Establish the vision for machine learning and data products at Coursera; define the next generation of how data science and machine learning can power content discovery, drive revenue, inform content sourcing, and scale high-quality teaching and learning at low cost. Develop and retain a world-class team of scientists; provide deep technical guidance in machine learning and statistics, including appropriate technical suggestions to data scientists as needed to facilitate their growth and improvement in data science and machine learning work and help them establish domain expertise. Partner with Data Engineering and Infrastructure Engineering to define and deliver on an architectural strategy that supports scalable machine learning applications in production, including for real-time serving of recommendations and learning interventions; guide Coursera in successful continued adoption of cloud-based machine learning solutions leveraging AWS technologies (e.g., SageMaker, EC2, S3, EMR) and complementary solutions like Databricks. Develop the team's breadth and depth of understanding in applied machine learning methodologies, including in the domains of natural language processing, reinforcement learning, and deep learning for personalized learning systems and scalable teaching. Develop the team's breadth and depth of understanding in applied statistical methodologies, including in the domains of optimization, forecasting, statistical inference, and experimentation to inform internal decision-making across learner and content platform investments and content sourcing. Develop the team's breadth and depth of coding languages for building machine learning and statistical inference applications including but not limited to Python, R, and SQL. Grow the team; manage the hiring pipeline, including resume screening, conducting interviews, building employer brand, attending hiring events, and working with the Recruiting team to support personnel decisions. Collaborate with Product Management, Engineering, and business leaders in defining and executing on the data product roadmap; propose novel machine learning solutions; discuss needs with stakeholders, secure the required resourcing, prioritize and drive forward on key initiatives, and run sprint planning meetings and standups. Build tools to increase team productivity like standardized feature stores and team-specific packages in scripting languages such as Python.
Job Requirements: Master's degree in Engineering, Data Science, or related field (or foreign equivalent) and three (3) years of experience in the job offered.
Travel Requirements: No
Manages others: Yes. 12 direct reports (9 full-time and 3 interns)
Supervisor/Manager: Emily Glassberg Sands, [email protected]
Special Requirements: Education or experience must include:
- Machine learning methodologies, including the domains of natural language processing, reinforcement learning, and deep learning;
- Experience in applied statistical methodologies, including the domains of optimization, forecasting, statistical inference, and experimentation;
- Experience in one scripting language for machine learning and statistical inference such as Python and R;
- SQL;
- Experience with cloud-based data engineering, data science, and machine learning environments such as leveraging AWS components like SageMaker, EC2, S3, EMR; Airflow; or Databricks; and
- Experience architecting machine learning solutions in production environments.
To apply: Visit https://www.jobpostingtoday.com/application/70529/apply
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