Data Scientist (Technical Leadership) at ChatGPT Jobs

Data Scientist - Meta

Location: Sunnyvale, CA

• On-site

Job Description: Meta is hiring experienced Data Scientists across product areas including Facebook, Instagram, Monetization, Integrity, Gen AI, Messenger, Reality Labs, WhatsApp, and others. You will collaborate with cross-functional partners on product and technical problems, using data to influence strategy and investment decisions.

Recruitment & Staffing

Responsibilities

• Work with large, complex datasets using analytical and statistical approaches.
• Apply technical expertise in quantitative analysis, experimentation, data mining, and data presentation to develop product strategies.
• Identify and measure product success through goal setting, forecasting, and monitoring key metrics.
• Identify opportunities for responsible use of cutting-edge technology.
• Define and test opportunities to improve products and drive roadmaps.
• Partner with Product, Engineering, and cross-functional teams to inform and execute product strategy.
• Navigate ambiguity by forming hypotheses, making data-driven decisions, and identifying areas for deeper investigation.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 8+ years of experience in data science or analytics, leading analytics work in an IC capacity, collaborating with Engineering and cross-functional partners, and guiding data-influenced product planning.
• Experience working with multiple stakeholders and cross-functional teams (Engineering, PM/TPM, Analytics, Finance).
• Strong framing and communication skills.

Preferred Qualifications

• Demonstrated ability to integrate AI tools to optimize workflows and drive measurable impact.
• Masters or Ph.D. in a quantitative field.
• Ongoing AI skill development (e.g., prompt/context engineering, agent orchestration).
• Experience with predictive modeling, machine learning, and experimentation/causal inference methods.
• Experience with responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality review).

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