Designs, develops, and deploys machine learning models and scalable data solutions to support advanced analytics and omnichannel marketing initiatives for Klick's clients.
Compensation
The base salary range for this position is between CAD $75,000 - $85,000.
Responsibilities
Design, build, and optimize data architectures, ETL pipelines, and feature engineering workflows using Python and SQL
Develop, train, evaluate, and deploy machine learning models using libraries such as scikit-learn, TensorFlow, or PyTorch
Process and analyze clickstream datasets and other large-scale data sources while maintaining and optimizing enterprise data warehouses and data lakes
Collaborate with data engineers, analysts, project managers, and client stakeholders to gather business requirements and deliver data-driven recommendations
Establish and document reproducible machine learning and data engineering workflows
Requirements
Master's degree or Ph.D. in Data Science, Machine Learning, Computer Science, Statistics, or a related quantitative discipline
Nice to have: AI enthusiasm
Nice to have: healthcare, pharmaceutical, life sciences, media, advertising, or agency environments
Nice to have: Power BI or comparable business intelligence platforms
Lead end-to-end data science projects, develop predictive models, and collaborate across engineering and business teams to deliver production-ready AI solutions.
Compensation
Compensation for this role is based upon experience, qualifications, location, and education and typically ranges from $140,000 to $160,000.
Responsibilities
Develop and implement statistical, predictive, optimization, machine learning, and AI solutions
Drive data science projects from discovery to deployment
Define scope, identify risks, and communicate progress
Build, validate, and evaluate predictive models
Develop production-ready ML solutions with reproducibility and monitoring
Requirements
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related quantitative field, or equivalent professional experience
Lead end-to-end machine learning projects to improve marketplace health and trust for Expedia Group.
Locations (2)
Austin, Texas, United States
Seattle, Washington, United States
Compensation
The total cash range for this position in Austin is $112,000.00 to $156,500.00. Employees in this role have the potential to increase their pay up to $179,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role. The total cash range for this position in Seattle is $112,000.00 to $156,500.00. Employees in this role have the potential to increase their pay up to $179,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.
Responsibilities
Own machine learning projects end to end, from understanding the business need and defining the approach through execution, launch, measurement, and iteration.
Develop and improve the Marketplace Health Model system to identify behaviors associated with trust, safety, and marketplace-health risks.
Translate complex marketplace challenges into scalable machine learning and statistical modeling solutions.
Software Engineer Data & AI II (Full Time) – United States
You are a passionate software engineer with expertise in developing scalable data pipelines, designing robust analytics platforms, and building infrastructure to support machine learning initiatives.
Locations (4)
San Jose, California, United States
New York, United States
Washington, United States
Illinois, United States
Compensation
The applicable full salary ranges for this position, by specific state, are listed below:
New York City Metro Area:
$125,700.00 - $203,100.00
Non-Metro New York state & Washington state:
$111,700.00 - $180,800.00
* For quota-based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined.
** Employees in Illinois, whether exempt or non-exempt, will participate in a unique time off program to meet local requirements.
