Staff Data Scientist, Marketing
Asana · San Francisco, CA · Business Data
About this role
Asana is hiring a staff-level Data Scientist based in San Francisco, CA (hybrid). The posting calls out experience with Python, R, SQL, Redshift and roughly 6+ years of relevant work. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $202,000–$282,000 per year.
- Role
- Data Scientist
- Function
- data engineering
- Level
- staff
- Track
- Tech leadership
- Employment
- Full-time
- Location
- San Francisco, CA
- Work mode
- Hybrid
- Experience
- 6+ years
- Education
- Bachelor's degree
- Department
- Business Data
More roles at Asana
Job description
from Asana careersThe Data Science team at Asana is pivotal in fulfilling our mission by fostering a data-driven approach in shaping both our product and business strategies. In your role on the Marketing Data Science team, you will be the deepest technical expert responsible for using data and scientific techniques to design and build scalable, state-of-the-art solutions to enhance Asana’s marketing effectiveness. You will drive the technical roadmap for data science, collaborating with marketing leadership and the broader Asana data community to uncover new opportunities. You will provide technical leadership and hands-on mentorship, elevating the team's technical bar and influencing overall business strategy through best-in-class modeling and experimental design.
This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.
What you’ll achieve:
- 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.