Senior Data Scientist at EPAM Systems Inc
Toronto, ON, Canada -
Full Time


Start Date

Immediate

Expiry Date

10 Oct, 25

Salary

70.0

Posted On

10 Jul, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Sql, Numpy, Computer Science, Gcs, Statistics, Pandas, Google Cloud, Aws, Data Science

Industry

Information Technology/IT

Description

We’re looking for a Senior Data Scientist to join our team and lead the development of machine learning models for campaign measurement and audience prediction. This role combines analytical expertise with hands-on development to drive our advertising effective solutions. You will have an opportunity to work at the forefront of retail media advertising technology, to collaborate with an innovative team dedicated to excellence. Our environment fosters growth through challenging projects, provides access to cutting-edge tools and methodologies, and enables you to create solutions that drive measurable business impact in the digital advertising space.
Req.#841791437

REQUIREMENTS

  • Degree in computer science, statistics or machine learning (Master’s degree is preferred)
  • 5+ years of experience in data science within retail/digital media, marketing or advertising
  • Strong proficiency with Python ecosystem (NumPy, Pandas) and SQL
  • Practical expertise with CI/CD tools (Git, GitHub, GitHub Actions)
  • Hands-on background with Google Cloud technologies (BigQuery, Vertex AI, GCS)
  • TensorFlow/PyTorch knowledge highly desirable
  • Google Cloud or AWS certifications a plus
Responsibilities
  • Develop and maintain ML models for measuring incremental impact of digital advertising campaigns
  • Build classification and regression models using Google Cloud tools (BigQuery, Vertex AI) and Python libraries (Pandas, NumPy, SciKitLearn)
  • Implement advanced media mix and multi-touch attribution models to optimize spending
  • Design frameworks for campaign measurement using synthetic controls and lift measurement techniques
  • Collaborate on MLOps integration, including CICD pipelines and monitoring systems
  • Perform complex data analysis and communicate findings to diverse stakeholders
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