asana
Staff Data Scientist, Marketing
At a Glance
- Location
- San Francisco
- Experience
- 6+ years
- Compensation
- base salary range is between $202,000 - $282,000. The actual base salary will
- Posted
- 2026-02-20T19:41:19-05:00
Key Requirements
Required Skills
Domain Knowledge
- Engineering
- Marketing
Benefits & Perks
ble and competitive benefits packages that support our employees worldwide a
Requirements
Bachelor Degree in Math, Statistics, Computer Science, Engineering a related quantitative field, or equivalent experience
6+ years of experience in a data science role, with 2+ years dedicated to technical leadership and mentorship of other data scientists, successfully driving the architecture and execution of large-scale production data science projects
4+ years of experience collaborating with Marketing functions on deep technical projects, with extensive experience designing, implementing, and deploying marketing models (e.g. MMM, LTV, MTA, Uplift)
Expert-level knowledge in advanced statistical modeling, causal inference, experimental design and analysis, and machine learning techniques relevant to marketing effectiveness
Proven track record developing, deploying, and maintaining scalable production ML solutions and data products
Technical Stack: Expert proficiency in SQL and Python. Experience with MLOps tools (e.g., MLFlow), statistical languages (e.g., R), and distributed data processing systems (e.g., Spark, Redshift) is a plus
Responsibilities
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.
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.
Collaborate with marketing leadership to pinpoint how data science can be further integrated into Asana's business approach.
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.
Develop and standardize MLOps tooling and processes that enable the team to deploy, monitor, and maintain multiple models in production efficiently and reliably.
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.