> Markdown version of [/jobs/ext/4613206-data-scientist](https://www.wearedevelopers.com/jobs/ext/4613206-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** The Meta Game, Inc.
- **Location:** Menlo Park, CA, United States
- **Salary:** $210,000.0 - $281,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Data Infrastructure, Query Languages, Python (Programming Language), Mathematical Software, SQL Databases, Scripting, Multi-Agent Systems, Prompt Engineering, Model Validation, Data Analytics, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_6cf0dcebd03d6711e5b058adb1751fd7

## About the Role

* 12+ years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python or R), and statistical or mathematical software for large-scale data analysis
 * Experience applying advanced statistical methods, including predictive modeling, causal inference, experimentation design, and time series forecasting in a product context
 * Experience translating data into product strategy and executive decision-making through data-driven narratives, written analytical frameworks, and stakeholder alignment across multiple cross-functional teams
 * Experience establishing analytical standards, data infrastructure best practices, or measurement frameworks that are adopted broadly across an organization or business domain, * Experience adhering to and implementing responsible, ethical AI practices, including risk assessment, bias mitigation, and quality and accuracy reviews
 * Experience defining and scaling self-service analytics platforms or data explorability tools that reduce time-to-insight for non-technical stakeholders
 * Demonstrated use of AI tools to accelerate analytical workflows, including responsible validation of AI-generated outputs and adherence to ethical AI practices
 * Background working with ambiguous and sparse data to operationalize harm prevalence measurement, causal inference, and root-cause analysis rooted in quantitative and statistical foundations
 * Demonstrated ability to integrate AI tools to optimize or redesign workflows and deliver measurable impact, such as efficiency gains or quality improvements
 * Experience in fast-paced, high-ambiguity, cross-functional environments, including the ability to move from agentic trace deep-dives to explaining risk dimensions in plain English for policy stakeholders
 * Demonstrated ongoing AI skill development, such as prompt or context engineering and agent orchestration, and staying current with emerging AI technologies

## Description

* Measure abuse: build statistical telemetry and measurement frameworks to detect and monitor policy violations or emergent harms
 * Scale model evaluation: design a scalable framework that adapts to evolving agentic model capabilities and the safety and risk landscape, grounded in statistical methods
 * Evaluate and optimize safeguards: scale offline and online performance evaluation of safeguards, using human-in-the-loop approaches and active learning
 * Design experiments and analyses: run controlled experiments and rollout studies to assess the impact of changes to policy or risk definitions and safety mitigations
 * Build intelligence and reporting: create monitoring signals, data pipelines, dashboards, and reporting to enable real-time monitoring that informs interventions and safety roadmaps