Staff Data Scientist, Marketing

Asana Asana · Enterprise · San Francisco, CA · Business Data

Staff Data Scientist on the Marketing Data Science team at Asana, responsible for designing and building scalable, state-of-the-art solutions to enhance marketing effectiveness using data and scientific techniques. This role involves driving the technical roadmap, providing technical leadership and mentorship, and developing MLOps processes. The focus is on marketing analytics, causal inference, and deploying ML models for business strategy.

What you'd actually do

  1. Architect, design, and lead the technical execution for the Marketing Data Science roadmap, serving as the Solution Architect for all core projects including Media Mix Modeling (MMM), User Lifetime Value, Causal Inferences, Multi-touch Attribution, and Spend Optimization engines.
  2. Act as the primary technical subject matter expert for the Marketing Data Science team, setting the technical bar for modeling quality, code rigor, data pipeline architecture, and solution scalability.
  3. Collaborate with marketing leadership to pinpoint how data science can be further integrated into Asana's business approach.
  4. Provide hands-on technical mentorship and guidance to a team of data scientists at varying levels, helping them navigate complex modeling challenges, choose appropriate methodologies, and establish robust ML Ops.
  5. Develop and standardize MLOps tooling and processes that enable the team to deploy, monitor, and maintain multiple models in production efficiently and reliably.
  6. Research, prototype, and advocate for emerging capabilities and state-of-the-art models in the marketing data science space, demonstrating their potential benefits and leading their implementation.
  7. Take on a technical leadership role within the broader Asana Data Community, interacting with Data Engineering and Platform teams to influence the data and MLOps infrastructure required to support marketing data products.

Skills

Required

  • SQL
  • Python
  • advanced statistical modeling
  • causal inference
  • experimental design and analysis
  • machine learning techniques relevant to marketing effectiveness
  • developing, deploying, and maintaining scalable production ML solutions and data products

Nice to have

  • MLOps tools (e.g., MLFlow)
  • R
  • Spark
  • Redshift
  • AI tools

What the JD emphasized

  • technical leadership
  • mentorship
  • production ML solutions
  • scalable production ML solutions
  • deploy, monitor, and maintain multiple models in production