Staff Data Scientist, Marketing

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

Staff Data Scientist role focused on enhancing marketing effectiveness through data science and ML modeling. Responsibilities include architecting roadmaps, providing technical leadership, mentoring junior scientists, and developing MLOps processes. The role requires expertise in advanced statistical modeling, causal inference, experimental design, and machine learning, with a focus on marketing-specific models like MMM, LTV, and MTA. Collaboration with marketing leadership and influencing data infrastructure are key aspects.

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.

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
  • technical leadership
  • mentorship

Nice to have

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

What the JD emphasized

  • marketing models (e.g. MMM, LTV, MTA, Uplift)