Data Scientist

The Meta Game, Inc.

Menlo Park, CA, United States

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Compensation

$210,000.0 - $281,000.0

Working hours

Regular working hours

Languages

English

Job source

www.juju.com

Tech stack

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

+1 more

Data Pipelines

Job 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

Requirements

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

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