Job Summary

As an Expert Data Scientist, become a part of a cross-functional development team engineering experiences of tomorrow. The Expert/Principal Data Scientist is a senior technical leader responsible for setting the standard for evaluation-driven development across the organizations AI/ML and analytics portfolio. This person defines modeling strategy, evaluation frameworks, and delivery governance across multiple 45-day release cycles, and is accountable for demonstrating measurable business impact at a portfolio level while mentoring senior and mid-level data scientists.

Responsibilities

Evaluation-Driven Development Strategy & Standards

- Define and champion the organizations evaluation-driven development framework: standards for selecting modeling approaches (statistical, ML, optimization, simulation, GenAI), evaluation metric design, and validation methodology
- Establish enterprise guidelines for aligning evaluation metrics to business and process outcomes, ensuring consistency across teams and products
- Set standards for the use of synthetic data, historical data, and real-world/controlled validation across the model lifecycle, including approval gates before production rollout
- Own the definition of "measurable improvement" for the portfolio ensuring every release is backed by a rigorous before/after comparison against business KPIs

Delivery Leadership

- Oversee and be accountable for multiple concurrent workstreams delivered through 45-day versioned agile cycles, ensuring the portfolio consistently ships incremental, measurable value
- Act as the escalation point for delivery risk, technical trade-offs,



and cross-team dependencies across release cycles
- Partner with product and engineering leadership to prioritize the roadmap based on expected business impact vs. delivery cost/risk

Platform, MLOps & Cost

- Set architectural and MLOps direction for AI/ML platforms and analytics products on AWS, including model deployment patterns, monitoring, and automated governance
- Own cost-management and performance-optimization strategy for data and ML workloads at scale, setting budgets and efficiency targets across teams
- Define enterprise approach to synthetic data generation, offline experimentation, and controlled real-world validation, including tooling selection

Governance & Compliance

- Own the framework for balancing model performance, explainability, regulatory compliance, and cost across the portfolio, including escalation criteria for high-risk models
- Represent the data science function in audits, regulatory reviews, and executive reporting on model governance and business impact

Leadership

- Mentor Senior Data Scientists and review their evaluation frameworks, modeling choices, and delivery outcomes
- Drive adoption of best practices in evaluation-driven development across the organization

Requirements

- Masters or PhD in Data Science, Statistics, Computer Science,



Operations Research, or related field (or equivalent demonstrated experience)
- 10+ years of experience delivering data science/ML/analytics solutions, including leadership of technical strategy at a portfolio or platform level
- Proven track record of selecting and justifying modeling approaches across statistical, ML, optimization, simulation, and GenAI paradigms based on business context
- Deep experience defining evaluation metrics tied to business and process KPIs, and demonstrating measurable outcomes from delivered models
- Extensive hands-on and architectural experience with AWS AI/ML platforms and MLOps practices at scale
- Demonstrated ownership of agile, versioned delivery cycles across multiple teams or products
- Solid track record balancing model performance, explainability, compliance, and cost trade-offs, including in regulated environments
- Experience designing synthetic data strategies, offline experimentation frameworks, and controlled real-world validation programs
- Excellent stakeholder management and executive communication skills

Desirable

- Experience presenting model governance and impact metrics to executive or regulatory audiences
- Publications, patents, or public speaking in applied ML/AI evaluation methodology
- Experience building or scaling MLOps platforms from the ground up

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Expert Data Scientist (India)
🏢 Ciklum
📍 India