Be a pioneer in business, education, and global impact by joining the Harvard Business School Digital Transformation team - a startup with assets, where you will have the chance to deploy cutting-edge digital- and emerging-technology education solutions.
Where else can you make a difference at the intersection of cutting-edge technology, world-class education, noble purpose, and timeless legacy?
As a Machine Learning Engineer, you will collaborate with data scientists, product managers, and data engineers to operationalize machine learning models in production and manage the lifecycle of artificial intelligence algorithms on a variety of domains.
You will develop and deploy novel approaches to optimize existing machine learning systems to maximize their business value.
Duties and Responsibilities :
- Architect, build, maintain, and improve new and existing suite of algorithms and their underlying systems.
- Automate machine learning pipelines and monitor and optimize machine learning solutions.
- Implement end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, performance tuning, and A / B testing.
- Use your entrepreneurial spirit to identify new opportunities to optimize business processes, improve consumer experiences, and prototype solutions to demonstrate value.
- Work closely with data scientists and analysts to create and deploy new product features online and in mobile apps.
- Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation.
- Write efficient and well-organized software to ship products in an iterative, continual-release environment.
- Contribute to and promote good software engineering practices across the team.
- Mentor and educate team members to adopt best practices in writing and maintaining production machine learning code.
- Actively contribute to and re-use community best practices.
- Monitor, debug, track, and resolve production issues.
- Work with project managers to ensure that projects proceed on time and on budget.
- Collaborate with Technical Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact.
- Complete other responsibilities as assigned.
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