Staff Data Scientist (AdTech)

TL;DR

Staff Data Scientist (ML/AdTech): Designing and architecting high-scale machine learning systems for digital advertising marketplaces with an accent on auction optimization and bidding strategies. Focus on building production-grade ML pipelines, solving complex marketplace problems, and providing technical leadership across multiple domains.

Location: Remote (US) or New York, NY

Salary: $195,500 - $218,500 a year

Company

hirify.global is a technology company focused on unleashing the economic potential of digital media by building high-scale ad marketplaces for publishers and advertisers.

What you will do

• Architect scalable, production-grade ML systems spanning training pipelines, real-time inference, and monitoring.
• Lead the evaluation and adoption of advanced AI/ML modeling techniques and research.
• Identify and drive high-impact cross-team initiatives to improve bidding strategies and prediction systems.
• Partner with product and engineering leaders to define the data science roadmap for platform capabilities.
• Mentor senior data scientists and establish technical standards and best practices across the organization.

Requirements

• Ph.D. with 6+ years or MS/BS with 8+ years of relevant industry experience.
• Deep expertise in deep learning and broad fluency across modern ML toolkits.
• Proven ability to architect and deliver complex ML systems that operate at scale.
• Mastery of probability, statistics, Python, and SQL.
• Experience with ML frameworks such as TensorFlow or PyTorch.
• Must be based in the United States

Nice to have

• Experience optimizing bidding algorithms for RTB environments.
• Proficiency with GCP and the Vertex AI platform.
• Experience with ML orchestration tools like TFX, Kubeflow, or Airflow.
• Familiarity with Java or Go.
• Background in digital media, MarTech, or AdTech.

Culture & Benefits

• Inclusive, team-oriented environment focused on shared mission and collaboration.
• Ownership-driven culture where employees are encouraged to drive results.
• Strong emphasis on transparency, continuous learning, and knowledge sharing.
• Commitment to equal employment opportunities and diversity.