Sr. Data Science Manager- Health at Twitter
San Francisco, CA, US

By applying for this role, you could choose to work in the following locations:
US - Remote US
San Francisco

What You’ll Do: Defending the health and integrity of the public conversation is Twitter’s top priority. The Health Data Science Team partners with Product, Engineering, and Policy to rigorously understand and quantify Twitter's highly complex and adversarial Health space and use this understanding to empower the Health Organization to better protect our users.

Who You Are: As a data science manager you build cohesive, high-functioning teams that thrive in a culture of trust, respect, and inclusion. You balance autonomy with guidance by giving your team the tools, context, confidence, and motivation to make decisions effectively and independently. You have the technical capacity to partner with tech leads and are comfortable diving into the fray to help drive resolutions in the case of bad incidents.

You take responsibility for the group’s short-term and long-term strategy, define the team's roadmap, success metrics, and priorities in close collaboration with multi-functional partners and inspire your team to independently do the same.

You are great at:

    Understanding consumer products and their vulnerability to manipulation and subversion
    Collaborating with PMs, engineers, and designers to drive product impact
    Leading and prioritizing projects
    Nurturing the career growth of data scientists

Requirements:

    6+ years of relevant experience in Data Science or adjacent roles.
    3+ years of experience managing Data Scientists
    Expertise solving complex and highly impactful quantitative problems with at least one scripting language (Python, R, etc.) and SQL.

Preferred Qualifications:

    A PhD or advanced degree in a quantitative field or commensurate experience
    Domain expertise in statistical inference
    Relevant Health or Platform Integrity domain expertise
    Work experience at a large tech company
    Expertise with quantitative analytics tools at scale with technologies such as Spark.