Competitive Intelligence Lead

Meta•Washington, DC

4d•$147,000 - $208,000

About The Position

As an Analyst in Meta’s Competitive Intelligence organization, you will operate at the intersection of analytics, data science, and market strategy. You will work on major projects and product areas (often in environments of significant ambiguity or technical complexity) to drive 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 knowledge, 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.

Requirements

Bachelors degree and a minimum of 4 years of work experience (minimum of 2 years with a Ph.D.) in business intelligence, product analytics, or economic / strategy consulting in a technology environment with increasing scope and impact.
Demonstrated skill to ethically source, validate, and synthesize high-signal insights from people (e.g., stakeholder interviews, skilled conversations, field research, and relationship-based information gathering) while maintaining high standards for privacy, consent, and integrity.
Proficiency in AI-powered tools: Demonstrate working knowledge of Generative AI technologies (e.g., LLM and AI agents) and experience designing, prompting, and orchestrating AI systems (e.g., prompt engineering) to automate data analyses, synthesize insights, and execute multi-step analytical tasks (e.g., prompting agent to clean datasets, build visualizations).
Practical working understanding of data-analytics tools, and direct experience managing, analyzing, manipulating and interpreting 1P and external 3P datasets.
Experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R).
Experience with statistical analysis, including causal inference (e.g., randomized control trials, quasi-experimentation such as synthetic control, diff-in-diff, meta-analyses) and/or bayesian aggregation (e.g., bayesian pooling, hierarchical modeling).
Demonstrated communication skills and experience presenting complex findings to both technical and non-technical stakeholders.
Demonstrated experience thriving in ambiguous environments and shaping new analytics organizations or products.

Nice To Haves

Master's or Ph.D. Degree in a quantitative field such as Quantitative Economics or Political Science, Operations Research, Data Science, Computer Science, Physics, Business, or Mathematics.
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements).
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.

Responsibilities

Influence organization-level product direction through data-driven narratives and an in depth understanding of the competitive landscape.
Conduct analyses with 3P datasets, develop statistical models and forecasts, and deliver actionable insights that inform market and business strategy.
Identify, onboard, and rigorously evaluate 3P datasets to determine their signal-to-noise ratio and predictive power.
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.
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.
Apply guidance from analyses to increase the accuracy of forecasts and better understand market trends.
Drive the adoption of appropriate methods and organization-wide best practices that raise the bar for the entire team.