Senior Staff Data Scientist

This is the evaluation and measurement seat behind Netradyne's AI, and the posting is refreshingly direct that it is not core model development. The team exists so that AI systems deployed at scale are measurable, trustworthy and decision ready, through evaluation frameworks, analytics platforms and KPI audits that shape product direction and real world safety outcomes. You would own the definition and evolution of the evaluation frameworks, metrics and analytical methodologies used to assess how ML features perform in actual deployments, run deep error and bias analysis to find failure modes, and own golden datasets, validation protocols and benchmarking standards. There is real statistical rigour attached: designing and reviewing offline evaluations, controlled rollouts and A/B tests, and getting bias, variance, confidence intervals and significance right. The posting also asks you to challenge existing metrics when they fail to capture ground reality.

What the posting requires as mandatory:
- A B.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 programming in Python, with solid fundamentals in object oriented programming, algorithms and data structures.
- Deep familiarity with SQL, complex query writing, indexing and database internals, plus working knowledge of at least one NoSQL store.
- Experience with data visualisation and analytical storytelling.
- Excellent written and verbal communication.
- Familiarity with AI powered tools for analytics and software development, including using them for exploratory analysis, experiment analysis and documentation, and critically evaluating AI generated output for correctness, statistical validity and reproducibility.

Preferred skills:
- Exposure to cloud platforms and services such as AWS Kinesis, EKS and autoscaling systems.
- Experience building lightweight web or service components with Flask or Django.
- Prior experience with large scale, noisy, real world datasets.

What the work actually looks like:
- Design, implement and maintain offline and online evaluation frameworks for AI and ML features.
- Define, validate and evolve the KPIs, success metrics and audit methodologies used across teams.
- Perform deep error analysis, bias analysis and segmentation to identify failure modes.
- Own golden datasets, validation protocols and benchmarking standards.
- Conduct large scale analytical studies on feature performance, data quality and system behaviour.
- Design and review experiments including offline evaluations, controlled rollouts and A/B tests, ensuring statistical correctness.
- Perform post deployment monitoring and regression detection.
- Build tools, dashboards and automation to scale audits and reporting.
- Mentor junior data scientists on statistical rigour, experiment design and analytical storytelling.

How this differs from the 5+ years version of this role published on this board on 2026-07-26: same team and same responsibilities, but this posting sets the bar at 8+ years rather than 5+. If you are between 5 and 8, look for the other one.

Location and working pattern: Bangalore. This posting does not state office days, though other Netradyne listings specify five days on site.

Honest fit guidance: if you want to build models, this is explicitly not that role. It suits someone happier proving whether a system works than building it, and willing to tell a product team its metric is wrong.