Machine Learning Engineer

CHEP


Date: 4 days ago
City: Mississauga, ON
Contract type: Full time

CHEP helps move more goods to more people, in more places than any other organization on earth via our 347 million pallets, crates and containers. We employ approximately 13,000 people and operate in 60 countries. Through our pioneering and sustainable share-and-reuse business model, the world’s biggest brands trust us to help them transport their goods more efficiently, safely and with less environmental impact.


What does that mean for you? You’ll join an international organization big enough to take you anywhere, and small enough to get you there sooner. You’ll help change how goods get to market and contribute to global sustainability. You’ll be empowered to bring your authentic self to work and be surrounded by diverse and driven professionals. And you can maximize your work-life balance and flexibility through our Hybrid Work Model.


Job Description


The Machine Learning Engineer will be responsible for developing machine learning capabilities and systems to support Asset Productivity. This will involve running tests and experiments on how we can identify in-efficiencies or anomalies in pallet declarations and flows. Any successful experiments will need to be productionised and automated to support Stakeholders.


Key Accountabilities


  • Develop machine learning solutions that support asset productivity.
  • Demonstrate an ongoing understanding of Machine Learning technologies in current marketplace and how they can be applied to the CHEP business.
  • Facilitate and assist in data gathering efforts and support testing during validation process.
  • Work with the Business Intelligence Developers & Data Analysts to collaborate on projects.
  • Make suggestions and recommendations and presenting these in a clear manner to stakeholders.
  • Work in an agile and iterative way.



Qualifications


  • Bachelor degree in Computer Science, Statistics or other Technical Degrees.



Experience



  • Minimum of 2 years Python programming experience in development of machine learning applications.
  • Experience in a diverse multi-cultural environment desired.
  • Successful delivery of machine learning applications to internal or external customers.



Skills and Knowledge



  • Excellent understanding of machine learning techniques and algorithms.
  • Knowledge of implementing Data Science or Machine Learning algorithms.
  • Experience with common statistical techniques and data science toolkits.
  • Great communication and influencing skills, with the ability to utilize data to justify business decisions.
  • Strong quantative and analytical skills and experience with data visualization tools.
  • Experience of working in an MLOps environment and working with Databricks.
  • Proficiency in using query languages (i.e. SQL) and big data analytics
  • Good applied statistics skills, such as distributions, statistical testing, regression, etc.
  • Fluency in English essential. Fluency in Spanish desirable.



Remote Type


Hybrid Remote


We are an Equal Opportunity Employer, and we are committed to developing a diverse workforce in which everyone is treated fairly, with respect, and has the opportunity to contribute to business success while realizing his or her potential. This means harnessing the unique skills and experience that each individual brings and we do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state, or local protected class.


Individuals fraudulently misrepresenting themselves as Brambles or CHEP representatives have scheduled interviews and offered fraudulent employment opportunities with the intent to commit identity theft or solicit money. Brambles and CHEP never conduct interviews via online chat or request money as a term of employment. If you have a question as to the legitimacy of an interview or job offer, please contact us at [email protected].


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