Netradyne - Senior Staff Data Scientist

- Netradyne harnesses the power of Computer Vision and Edge Computing to revolutionize the modern-day transportation ecosystem.

- We are a leader in fleet safety solutions.

- With growth exceeding 4x year over year, our solution is quickly being recognized as a significant disruptive technology.

- Our team is growing, and we need forward-thinking, uncompromising, competitive team members to continue to facilitate our growth.

Job Overview:

- Our team is responsible for ensuring that AI systems deployed at scale are measurable, trustworthy, and decision- ready.

- We build rigorous evaluation frameworks, analytics platforms, and KPI audits that directly influence product direction and real- world safety outcomes.

- This role is primarily evaluation and analytics driven.

- As a Staff Data Scientist will own the definition, execution, and evolution of evaluation frameworks, metrics, and analytical methodologies used to assess AI/ML feature performance in real- world deployments.

- Rather than focusing on core model development, this role emphasizes measurement rigor, error analysis, experimentation, and decision support-ensuring that metrics accurately reflect system behavior, business impact, and safety outcomes.

Key Responsibilities:

Evaluation & Measurement Ownership:

- Design, implement, and maintain offline and online evaluation frameworks for AI/ML features.

- Define, validate, and evolve KPIs, success metrics, and audit methodologies used across teams.

- Perform deep error analysis, bias analysis, and segmentation to identify failure modes and improvement opportunities.

- Own golden datasets, validation protocols, and benchmarking standards.

Analytics & Insight Generation:

- Conduct large-scale analytical studies to understand feature performance, data quality issues, and system behavior.

- Translate complex analytical findings into clear, actionable insights for engineering, product, and leadership stakeholders.

- Challenge existing metrics or evaluation approaches when they fail to capture ground reality.

Experimentation & Statistical Rigor:

- Design and review experiments including offline evaluations, controlled rollouts, and A/B tests.

- Ensure statistical correctness in analysis, including bias, variance, confidence intervals, and significance.

- Perform post- deployment monitoring and regression detection.

Tooling, Automation & Scale:

- Build and maintain tools, dashboards, and automation frameworks to scale audits, evaluations, and reporting.

- Improve repeatability, reproducibility, and reliability of analytics pipelines.

- Enable self- serve analytics and standardized reporting for broader teams.

Leadership & Ownership:

- Independently identify gaps in evaluation, metrics, or data quality and drive solutions end- to- .

- Act as a technical lead to lead a team of junior data scientists on statistical rigor, experiment design, and analytical storytelling.

Mandatory Skills:

- Tech, M.Tech, or PhD in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field.

- 8+ years of experience in data science, analytics, or a closely related domain.

- Strong foundation in probability, statistics, and estimation theory.

- Strong analytical and problem- solving skills with keen attention to detail.

- Strong programming skills in Python, with solid fundamentals in OOP, algorithms, and data structures.

- Deep familiarity with SQL, complex query writing, indexing, and database internals; working knowledge of at least one NoSQL data store.

- Experience with data visualization and analytical storytelling.

- Excellent written and verbal communication skills.

- Familiarity with AI- powered tools for analytics and software development, including:.

- Using AI tools for exploratory data analysis, feature ideation, experiment analysis, and documentation.

- Leveraging AI assistance for rapid prototyping, code refactoring, debugging, and analytical workflows.

- Ability to critically evaluate AI- generated outputs for correctness, statistical validity, reproducibility, and production readiness.

Preferred Skills:

- Exposure to cloud platforms and services (AWS Kinesis, EKS, autoscaling systems).

- Experience building lightweight web or service components using Python frameworks (Flask, Django).

- Prior experience working with large-scale, noisy, real-world datasets.

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