The Position
You will work closely with our partners in Product, Engineering, Design, and Research to understand customer behavior, inform product decisions, and increase healthy participation on Twitter. You will support the entire product development lifecycle from product ideation to opportunity sizing, from measurement design to experimentation, from causal analysis to post-launch learning, and iteration into the next development cycle. You will analyze how changes to the platform affect customer behavior and business outcomes. You will also communicate findings to cross-functional partners and use data to improve the team’s strategy.
What you’ll be doing
As a staff data scientist on the Data Science team you will:
- Design, implement, and analyze experiments to determine how changes to the platform affect user behavior and refine our theoretical understanding of users’ interests and the communities they form.
- Conduct analyses to derive strategic insights from vast datasets across different product areas.
- Create accurate and statistically sound summary metrics to evaluate the performance of features/products and motivate teams’ targets/Key Results (KRs).
- Apply sophisticated statistical techniques to model user behavior, identify causal impact and attribution, build and benchmark metrics, and forecast future performance.
- Communicate findings to executives and cross-functional product teams by translating statistically rigorous analyses towards business impact.
- Improve the reproductivity and efficiency of analysis work through code review and other established processes.
- Help drive the team roadmap, encourage technical excellence, and train and mentor junior team members.
Qualifications
What it takes
- PhD in a quantitative field and 4+ years of experience (or 7+ years of total post-graduate experience).
- Experience with one or more of the following in an applied business setting: advanced statistical techniques for A/B testing, methods for experimental design, observational causal inference, or time series forecasting.
- Track record of executing independent projects and leading complex, multi-functional projects with several dependencies.
- Strong proficiency with Python / R and SQL.
- Solid written and verbal communication skills, especially for executive audiences.
- Experience mentoring junior data scientists and enthusiasm for building out a new data science community.