Data Scientist, Product Analytics
Meta · United States
- Hybrid
- Full-time
- $175,000 / year
- United States
Job highlights
- Shape products used by billions globally.
- Analyze complex datasets with advanced techniques.
- Influence product strategy with data-driven insights.
- Collaborate with diverse cross-functional teams.
- Grow within a world-class analytics community.
About the role
About the Role
As a Data Scientist, Product Analytics at Meta, you will play a pivotal role in shaping the future of our people-facing and business-facing products across the entire Meta family of applications, including Facebook, Instagram, Messenger, WhatsApp, and Oculus. Leveraging your technical expertise, analytical acumen, and product intuition on one of the world's richest datasets, you will define user experiences for billions of people and millions of businesses globally. You will collaborate with a diverse range of cross-functional partners, including Product, Engineering, Research, Data Engineering, Marketing, Sales, and Finance, to tackle complex product and business challenges. Your work will involve using data and analysis to identify and solve the most significant challenges in product development, influencing product strategy and investment decisions with a strong focus on impact.
Product Leadership
You will utilize data to guide product development, quantify new opportunities, anticipate upcoming challenges, and ensure that our products deliver significant value to users, businesses, and Meta. You will assist your partner teams in prioritizing development efforts, setting strategic goals, and understanding their product ecosystems.
Analytics Expertise
You will lead teams by providing data-driven insights. Your focus will be on developing hypotheses and applying a comprehensive toolkit of rigorous analytical approaches, diverse methodologies, frameworks, and technical strategies to test them effectively.
Communication and Influence
Your role extends beyond presenting data; you will craft compelling data-driven narratives. You will persuade and influence your partners through clear insights and actionable recommendations, building credibility through structured and clear communication to become a trusted strategic partner.
Data Scientist, Product Analytics Responsibilities
- Work with large and complex datasets to address a wide range of challenging problems using various analytical and statistical approaches.
- Apply technical expertise in quantitative analysis, experimentation, data mining, and data presentation to develop strategies for products serving billions of people and hundreds of millions of businesses.
- Identify and measure the success of product initiatives through goal setting, forecasting, and monitoring of key product metrics to understand trends.
- Define, understand, and test opportunities and levers to improve products, driving roadmaps through your insights and recommendations.
- Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent.
- 4+ years of work experience in analytics, data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical mathematical software (e.g., R). A minimum of 2 years of experience is required with a Ph.D.
- 4+ years of experience in solving analytical problems using quantitative approaches, understanding ecosystems, user behaviors & long-term product trends, and leading data-driven projects from definition to execution (including defining metrics, experiment design, and communicating actionable insights).
Preferred Qualifications
- Master's or Ph.D. Degree in a quantitative field.
- 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.
About Meta
Meta builds technologies that help people connect, find communities, and grow businesses. Since Facebook's launch in 2004, we've empowered billions globally through apps like Messenger, Instagram, and WhatsApp. We are now moving beyond traditional screens towards immersive experiences in augmented and virtual reality to pioneer the next evolution in social technology. Join Meta to help shape a future that transcends digital connection, breaking free from screen constraints, geographical distances, and even the laws of physics.
Meta is an Equal Employment Opportunity and Affirmative Action employer. We value diversity and do not discriminate based on race, religion, color, national origin, sex, sexual orientation, gender identity, gender expression, age, veteran status, disability, or any other legally protected characteristic. We consider qualified applicants with criminal histories. Meta participates in E-Verify where required by law. Note: Meta may use AI and machine learning in its hiring process. We are committed to providing reasonable accommodations for candidates with disabilities. If you need assistance, please contact accommodations-ext@meta.com.
Key skills/competency
- Data Scientist
- Product Analytics
- SQL
- Python
- Statistical Analysis
- Experimentation
- Data Mining
- Quantitative Analysis
- Machine Learning
- AI Integration
Skills & topics
- Data Scientist
- Product Analytics
- Analytics
- SQL
- Python
- Statistics
- Experimentation
- Data Mining
- Quantitative Analysis
- Machine Learning
- AI
- Messenger
- Oculus
How to get hired
- Tailor your resume: Highlight your 4+ years of analytics experience, SQL, Python, and quantitative problem-solving skills. Emphasize your experience in defining metrics and driving data-driven projects from start to finish for Data Scientist, Product Analytics roles.
- Showcase AI proficiency: If applicable, detail your experience integrating AI tools, adhering to ethical AI practices, and developing AI skills to demonstrate your readiness for modern workflows.
- Quantify your impact: When describing past projects, use numbers to demonstrate the scale of data you've worked with and the impact of your insights on product development or business outcomes.
- Prepare for technical interviews: Be ready to discuss your experience with large datasets, statistical approaches, experimentation, and SQL/Python. Practice coding challenges and explain your thought process clearly for the Data Scientist, Product Analytics position.
- Understand Meta's culture: Research Meta's mission, products (Facebook, Instagram, etc.), and their focus on connecting people and communities. Align your application with their values of moving fast and building impactful products.
Technical preparation
Behavioral questions
Frequently asked questions
- What is the typical career progression for a Data Scientist, Product Analytics at Meta?
- At Meta, Data Scientists, Product Analytics typically progress by deepening their expertise in product strategy and analytics, potentially moving into senior or lead roles. Career paths can also involve specialization in areas like experimentation, causal inference, or AI integration, or transitioning into management roles within the analytics or product teams. Meta emphasizes continuous learning and offers opportunities for skill development and internal mobility.
- What kind of data challenges can I expect as a Data Scientist, Product Analytics at Meta?
- You can expect to work with exceptionally large and complex datasets from Meta's suite of applications (Facebook, Instagram, etc.). Challenges include analyzing user behavior at scale, designing and interpreting A/B tests for new features, forecasting product metrics, identifying drivers of user engagement and retention, and contributing to the development of new products and features by providing deep analytical insights.
- How important is experience with AI tools for this Data Scientist, Product Analytics role at Meta?
- While not strictly mandatory for the minimum qualifications, demonstrated ability to integrate AI tools and adhere to ethical AI practices is a significant plus (preferred qualification). Meta is actively incorporating AI into its products and workflows, so candidates with experience in prompt engineering, agent orchestration, or optimizing workflows with AI will be highly valued.
- What are Meta's expectations regarding collaboration for a Data Scientist, Product Analytics?
- Collaboration is central to this role. You will work closely with Product Managers, Engineers, Researchers, Data Engineers, and Marketing/Sales teams. The expectation is that you will not only provide data insights but also actively partner with these teams to inform strategy, influence decisions, and help execute product roadmaps, acting as a trusted advisor.
- How does Meta approach work-life balance for its Data Scientists?
- Meta, like many tech companies, emphasizes impact and fast-paced development. While specific work-life balance can vary by team and individual, the company culture generally supports flexibility. Employees are encouraged to manage their time effectively to deliver on ambitious goals. Benefits and resources are available to support employee well-being.
- What is the role of SQL and Python in the Data Scientist, Product Analytics position at Meta?
- SQL is fundamental for data extraction and manipulation from Meta's vast databases. Python is crucial for data analysis, statistical modeling, machine learning, experimentation, and automation of analytical tasks. Proficiency in both is essential for effectively tackling the data challenges in this role.
- Does Meta encourage continuous learning for Data Scientists?
- Absolutely. Meta fosters a culture of continuous learning and skill development, particularly within its analytics community. Opportunities include internal training, access to resources, cross-team knowledge sharing, and support for staying current with the latest advancements in data science, analytics, and AI.