Staff Data Scientist - Ads (AI Native)
New
J
JobgetherAdvertising Technology
United StatesFull-TimeStaff
SalaryCanada-based salary range: $198,000 - $233,000 CAD. Equity opportunities as part of the total compensation package.
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSPythonSQLKubeflowMachine LearningMLFlowAirflowSparkdbt
Requirements
- Advanced degree in a quantitative field or equivalent professional experience.
- 8+ years of experience designing, implementing, and operating machine learning and optimization systems.
- Strong Python programming skills with expertise in software engineering best practices, including testing, modular design, and version control.
- Experience with ML lifecycle and data processing tools such as MLflow, Kubeflow, SparkML, SQL, Spark/PySpark, dbt, or Airflow.
- Practical experience working within major cloud platforms such as AWS, GCP, or Databricks, including knowledge of cloud infrastructure, networking, security, and storage.
- Experience deploying and operating machine learning models in production environments.
- Strong understanding of data pipelines, experimentation frameworks, and scalable ML architecture.
- Experience leading technical projects and driving initiatives from concept through production.
- Hands-on experience using AI tools as part of daily development workflows, including delegating implementation tasks and reviewing AI-generated outputs.
- Strong problem-solving mindset with the ability to structure complex challenges before selecting solutions.
- Excellent communication skills with the ability to influence cross-functional stakeholders.
Responsibilities
- Partner with Product, Data Science, Cloud Engineering, and Data Engineering teams to design, develop, and deploy machine learning and optimization solutions.
- Build, train, deploy, and scale ML models through high-availability services or batch processing workflows.
- Develop algorithms that improve advertising performance, including delivery efficiency, optimization, and system scalability.
- Collaborate with engineering teams to integrate model outputs directly into production advertising systems.
- Establish monitoring, logging, alerting, and performance tracking frameworks for ML systems, including inference performance, latency, resource utilization, and model drift.
- Improve data ecosystems by partnering with data engineering teams to create scalable pipelines for experimentation and machine learning workflows.
- Implement robust data, code, and model lineage practices to support reliability, compliance, reproducibility, and security.
- Leverage AI-native development tools to accelerate implementation, automate workflows, and increase engineering velocity.
- Review and validate AI-generated code, analysis, and models to ensure production quality and technical excellence.
- Mentor other data scientists and contribute to ML architecture decisions, technical standards, and team best practices.
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