Data Scientist

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

Data Scientist role focused on enhancing marketing effectiveness through data and scientific techniques. Responsibilities include architecting and leading the technical execution of the marketing data science roadmap, setting technical standards, providing mentorship, developing MLOps tooling, and researching emerging capabilities. The role requires expertise in advanced statistical modeling, causal inference, experimental design, and machine learning, with a proven track record in deploying production ML solutions.

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. 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.
  4. Develop and standardize MLOps tooling and processes that enable the team to deploy, monitor, and maintain multiple models in production efficiently and reliably.
  5. 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.

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)
  • statistical languages (e.g., R)
  • distributed data processing systems (e.g., Spark, Redshift)

What the JD emphasized

  • state-of-the-art solutions
  • technical roadmap
  • technical leadership
  • best-in-class modeling
  • core projects
  • primary technical subject matter expert
  • technical bar
  • robust ML Ops
  • state-of-the-art models
  • technical leadership role
  • production ML solutions
  • marketing models

Other signals

  • marketing effectiveness
  • data-driven approach
  • MLOps
  • production ML solutions