Christa Hammond

Experienced people leader growing Data Scientists to do the most impactful work of their careers

My career started as a hands-on Data Scientist with end-to-end data product exposure, from R&D to client delivery experience, and I've been leading Data Science teams for nearly 7 years now. I specialize in driving data-driven business impact through communicating technical results with internal & external stakeholders, and in growing teams of technical folks to do the same.


I've led teams at Oracle ($1.2B Datalogix acquisition) and Facebook, producing industry best-practices for large an…

Christa Hammond

Work

Experience

  • Windfall

    Windfall

    Director, Data Science

    Jul 2021 - Jul 2023

    Windfall is a people intelligence and AI company that gives go-to-market teams actionable insights. By democratizing access to people data, organizations can intelligently prioritize go-to-market resources to drive greater business outcomes. Powered by best-in-class machine learning and propensity modeling, Windfall activates insights into workflows that engage the right people for non-profits, as well as B2B and B2C corporations.

    • Transformed an underperforming Data Science team of 5 into a productive organization of 8. Made hiring/firing decisions, conceived and implemented a clear performance management framework, and coached team members for growth and promotion.
    • Structured the department into 2 independent teams: one ran a ML models in production, and the other focused on data quality and product R&D.
    • Upleveled 2 team members to directly manage these teams, and established coordination and goal alignment with engineering talent.
    • Established regular communication channels with executive leadership (CEO, CTO, and VP of Product) to keep the team accountable and aligned with company strategy. Worked closely with these leaders to drive the release of 10+ new products and features.
    • Represented the Data Science org in presentations given to the full company, in client meetings, and in thought-leadership webinars to the industry.
    • Drove efficiencies in our ML deliverables, reducing turnaround time by 3–4⨉ & fail rate by over 5⨉.
    • Managed roadmap planning & execution for all of Data Science, in partnership with Product. Led team Agile ceremonies.
    • Achieved company culture award for Excellence in Communication.
  • Facebook

    Facebook

    Data Science Manager

    Jan 2020 - Jul 2021

    Facebook is the largest social media platform in the world, with nearly 3 billion monthly active users. The Auction Experts team at Facebook (now Meta) focused on optimizing advertiser value for individual advertisers across several verticals and products, by recommending best practices for digital advertisement bidding, targeting and optimization methods.

    • Guided a team of 4–5 Data Scientists in their research to produce best practices for advertisers navigating Facebook’s ad auction, including methodology review. Methods include tree-based models, simple/logistic regression, and clustering techniques.
    • Managed stakeholder priorities up to VP level in Client Solutions, Measurement, and Product Marketing across 4 industry verticals. Developed team understanding of industry-specific needs.
    • Communicated results and implications with cross-functional partners regularly across verticals and global regions. Briefed business leaders on trends, growth opportunities, and recommendations.
    • Developed team members to take on projects of increasing complexity and scope. Removed roadblocks and provided hands-on guidance in communication and visibility to activate data-driven recommendations.
    • Completed regular performance reviews, delivered compensation increases, interviewed candidates, and contributed to changes in organizational structure of the broader team (size >30).
  • Oracle Data Cloud

    Oracle Data Cloud

    Data Science Manager

    Jan 2017 - Jan 2020

    Oracle Data Cloud (now Oracle Advertising) is made up of 5 ad tech acquisitions: BlueKai, Datalogix, AddThis, Crosswise, and Grapeshot. This division of Oracle provides solutions for audience creation, an identity platform, digital measurement, and contextual intelligence together in one service.

    • Guided a team of 4–5 Data Scientists in their research to produce best practices for advertisers navigating Facebook’s ad auction, including methodology review. Methods include tree-based models, simple/logistic regression, and clustering techniques.
    • Managed stakeholder priorities up to VP level in Client Solutions, Measurement, and Product Marketing across 4 industry verticals. Developed team understanding of industry-specific needs.
    • Communicated results and implications with cross-functional partners regularly across verticals and global regions. Briefed business leaders on trends, growth opportunities, and recommendations.
    • Developed team members to take on projects of increasing complexity and scope. Removed roadblocks and provided hands-on guidance in communication and visibility to activate data-driven recommendations.
    • Completed regular performance reviews, delivered compensation increases, interviewed candidates, and contributed to changes in organizational structure of the broader team (size >30).
  • Datalogix / acquired by Oracle Data Cloud

    Datalogix / acquired by Oracle Data Cloud

    Data Scientist

    Jun 2014 - Jan 2017

    Datalogix was an ad tech startup offering audience, measurement, and identity solutions to digital and direct mail advertisers, primarily in the CPG, Retail, and Auto industries. Datalogix was acquired by Oracle in 2015 ($1.2B exit, a 9.6x revenue multiple valuation) to become the largest piece of Oracle Data Cloud (now Oracle Advertising).

    • Oversaw design, training, and accuracy of all statistical analysis performed by the team, from mixed modeling to metric creation and ad hoc optimization algorithms.
    • Scope included constructing models, evaluating and defining metrics, identifying trends, writing back-end functions, and communicating data-driven narratives and recommendations with clients.
    • Influenced product decisions using exploratory statistical analyses of both sparse and large data sets, often with missingness, high dimensionality, noise, multicollinearity, and non-normality.
    • Developed production code to build out & improve three major product lines.

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