Senior Data Scientist

The Kraft Heinz Company•Toronto, ON

19d•CA$96,200 - CA$120,200•Hybrid

About The Position

Kraft Heinz is seeking a talented and driven Senior Data Scientist with expertise in machine learning and a passion for building robust pipelines for demand forecasting. This role is a key contributor on the demand forecasting team, responsible for building and testing statistical and machine learning models to accurately predict demand from retailers. The models developed will be deployed into a production setting to drive value across all brands. The Senior Data Scientist will provide updates to the Product Owner and Data Science Lead, and partner with business users to identify modeling opportunity areas.

Requirements

2+ years experience with predictive modeling time-series, machine learning, statistical modeling
Experience building demand forecast models
Proficiency in programming languages such as Python and R, as well as libraries like scikit-learn
Knowledge on factors that influence shipment demand
Proficient in SQL and working with relational databases.
Experience using cloud-based services such as AWS, Azure, or Google Cloud
Excellent problem-solving skills and ability to think critically about complex business challenges.
Strong communication skills
Proven ability to work effectively in a collaborative, fast-paced environment.

Responsibilities

Build and test statistical and machine learning models to accurately predict demand from retailers.
Deploy models into a production setting.
Provide updates and results to the Product Owner and Data Science Lead on the progress of work.
Partner with business users to identify modeling opportunity areas.
Utilize strong analytical skills and machine learning expertise to develop advanced time-series models for demand forecasting.
Collaborate with cross-functional teams to identify and define business problems related to demand forecasting.
Conduct exploratory data analysis and feature engineering to extract valuable insights from complex datasets.
Develop and implement machine learning algorithms to optimize demand forecasting accuracy and efficiency.
Evaluate and fine-tune models by applying statistical methods and running experiments on real-world data.
Communicate findings and insights to team members and work with the team to define modeling next steps.