Description:
The Machine Learning Operations (MLOps) is a new team and is instrumental in guiding the Advanced Analytics journey at Kraft Heinz. As a part of this highly energetic team you will lead the team creating/maintaining model pipelines and putting models into production. You will also get opportunities to expand your horizons working with the data scientists to develop ML models and best in class architecture using industry advanced practices.
If you are passionate about applying technology to solve practical business problems, developing intelligent tools, and working with a friendly team to drive business value, this is going to be your dream. The sky is the limit in terms of enhancing your knowledge and working on a variety of projects alongside the Data Science team at Kraft Heinz!
Primary Responsibilities
- Lead, manage and develop the ML Engineering group in the Toronto office
- Responsible for the internal and external labor applied to ML Engineering projects.
- Manage the ML engineering budget and estimation process.
- Implement machine learning algorithms and customized libraries
- Assist Data Science team with development of complex tools, models or database builds
- Collaborate with data engineers and data scientists to develop data and model pipelines
- Improve existing Machine Learning models
- Write production-level code
- Bring code to production and engage in code reviews
- Use of problem-solving, advanced Analytics methodologies (ML/AI) knowledge to derive architecture of ML models and assist development of new models
- Responsible for ongoing operation for existing models.
- Working with Data Engineers & Data Scientists to ensure integrity & governance, rituals and routines
- Provide inputs/stories and assist in prioritization of Data Engineering sprints
Qualifications
- Advanced Statistics, Business, Information Technology experience required
- Understanding of the major modelling techniques are how to apply them (Linear and Logistics Regression, Bayesian, Time series, Confidence interval, Deep Learning etc.)
- Experience in leading technical teams delivering and supporting analytical capabilities.
- Ability to support ML models in a production cloud environment (Azure experience preferred)
- "Hands-on” experience with analytical tools (preferably AzureML, Vertex, SageMaker, Snowflake, DataBricks, Tableau, Alteryx, SQL, Python, and/or R)
- Solid understanding on LLM operations (including fine tuning and grounding techniques) and prompt engineering best practices
- Strong experience developing CI/CD pipelines
- Proficient in version control tools and practices (Github, Azure DevOps).
- Financial and Project management experience and strong interpersonal skills
- Self-Starter, driven with high business process foresight
- Great Teammate with a positive attitude and ability to work across different business collaborator and technical teams to accomplish complex tasks
- Experience working with database systems and analytical tools
- Strong communication, problem-solving skills and a strong team player