Senior Data Scientist

Senior Data Scientist
📍 Location: Remote, US
🏢 Industry: Technology, Information and Internet
💼 Work Setting: Hybrid
Are you an experienced Data Scientist with deep expertise in statistics, machine learning evaluation, experimentation, and large-scale data analysis?
We are seeking a Senior Data Scientist to help evaluate and improve autonomous vehicle (AV) performance, large-scale machine learning systems, and simulation environments. This role plays a critical part in measuring the safety, reliability, and deployment readiness of autonomous driving technologies through advanced statistical methods, performance metrics, and data-driven decision making.
The ideal candidate combines expertise in statistics, machine learning, experimentation, simulation analytics, Python, SQL, and autonomous systems evaluation while collaborating closely with Product, Engineering, and Research teams.
Key Responsibilities
Autonomous Vehicle Performance Evaluation
Develop evaluation methodologies for autonomous vehicle systems.
Create frameworks to measure driving performance and safety outcomes.
Assess real-world and simulation-based vehicle behavior.
Support deployment readiness decisions through rigorous analysis.
Establish performance benchmarks for autonomous driving technologies.
Focus Areas
Autonomous Vehicle Evaluation
Safety Metrics
Performance Assessment
Deployment Readiness
System Validation
Machine Learning Evaluation
Design frameworks for evaluating large-scale machine learning models.
Develop metrics that measure model effectiveness and reliability.
Analyze model behavior across diverse scenarios.
Investigate model performance trends and edge cases.
Provide recommendations for system improvements.
Responsibilities
Model Evaluation
Performance Measurement
Benchmarking
Reliability Analysis
Quality Assessment
Simulation Analytics
Develop methods for measuring simulation quality and accuracy.
Compare simulation results against real-world driving outcomes.
Validate simulation environments used for testing autonomous systems.
Establish statistical confidence in simulation-driven decisions.
Support continuous improvement of simulation platforms.
Statistical Research & Innovation
Develop advanced statistical methodologies for unique AV challenges.
Design approaches to evaluate:
Rare Events
Safety-Critical Scenarios
Synthetic Data Performance
Real-World Driving Data
Simulation Accuracy
Create innovative analytical approaches where standard methods are insufficient.
Advance the scientific rigor of autonomous vehicle evaluation.
Example Areas
Rate Estimation
Bayesian Methods
Uncertainty Quantification
Experimental Design
Statistical Inference
Metrics Development & Trend Analysis
Build new performance metrics and KPIs.
Analyze operational and simulation datasets.
Identify meaningful trends, anomalies, and risk indicators.
Translate complex data into actionable recommendations.
Improve organizational understanding of system performance.
Problem Solving & Research Leadership
Frame complex and ambiguous technical problems.
Define analytical priorities and research approaches.
Innovate on statistical methods and evaluation frameworks.
Drive data-informed decision making across major initiatives.
Establish yourself as the subject matter expert for designated project areas.
Cross-Functional Collaboration
Partner closely with:
Product Managers
Machine Learning Engineers
Software Engineers
Simulation Engineers
Autonomous Vehicle Researchers
Technical Leadership
Responsibilities
Influence technical decisions through data.
Support product deployment reviews.
Evaluate engineering trade-offs.
Promote evidence-based decision making.
Stakeholder Communication
Present analytical findings to technical and executive audiences.
Translate statistical concepts into business and engineering insights.
Deliver clear recommendations backed by data.
Support strategic roadmap decisions through quantitative analysis.
Mentorship & Technical Leadership
Mentor junior and mid-level data scientists.
Conduct technical reviews and provide feedback.
Share best practices across the organization.
Contribute to the advancement of data science capabilities.
Foster a culture of analytical excellence.
Qualifications
Education
Required
Degree in a quantitative field such as:
Statistics
Mathematics
Physics
Operations Research
Data Science
Computer Science
Related Quantitative Discipline
Experience
Required
One of the following:
5+ years of industry experience solving data science problems
OR
PhD in a quantitative field plus 3+ years of industry experience
Technical Skills
Statistics & Analytics
Required
Expert-level knowledge in:
Statistical Modeling
Statistical Inference
Experimental Design
Hypothesis Testing
Predictive Analytics
Data Analysis
Preferred
Bayesian Statistics
Time-Series Analysis
Rare Event Modeling
Simulation Analytics
Machine Learning
Required
Machine Learning Evaluation
Model Validation
Performance Measurement
Model Benchmarking
Preferred
Large-Scale ML Systems
Applied AI
Predictive Modeling

Work arrangement
Yes