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Meta

Competitive Intelligence Lead

Remote • Chicago, United States • Lead • $177–247k / год
Зарплата
$177–247k / год
Опубликовано
13 ч назад
Описание

As a Competitive Intelligence Lead in Meta’s Competitive Intelligence organization, you will operate at the intersection of advanced analytics, data science, and market strategy. You will lead major projects and product areas—often in environments of significant ambiguity or technical complexity—driving both technical and business outcomes. This is a hands-on, high-impact role for builders who thrive on solving real problems. This role demands a unique blend of analytical and statistical expertise, strategic thinking , and the ability to translate complex insights into impactful product and business decisions. You will be recognized as a thought partner by cross-functional leads and will help shape the analytical foundations that inform how we build and grow our products.

Competitive Intelligence Lead Responsibilities:

  • Market Strategy: Influence organization-level product direction through data-driven narratives and an in depth understanding of the market landscape. Demonstrated experience operating at scale, and across ambiguous, environments with working knowledge of econometrics. As a quantitative market-strategist, you will blend practical and applied understanding with technical expertise, including pressure-testing data for quality, reliability, understanding data-biases and being solution driven
  • Analytics Leadership: Conduct advanced analyses with 3P datasets, develop statistical models and forecasts, and deliver actionable insights that informs market and business strategy. These include, but are not limited to:
  • Data onboarding: Identify, onboard, and rigorously evaluate 3P datasets to determine their signal-to-noise ratio and predictive power
  • Data triangulation: Triangulate data from many sources of imperfect information. Synthesize multiple, low-fidelity 3rd-party signals into a single high-fidelity trend report using Bayesian aggregation or other methods
  • Data transformation: Apply quasi-experimental designs (e.g., synthetic control, diff-in-diff) to isolate the impact of exogenous market shocks and competitor actions on internal performance metrics, using 3rd-party behavioral and economic datasets
  • Insight and implications: Apply guidance from such analyses to incre...

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