Below exp is mandatory:

Modeling approaches(statistical, ML, optimization, simulation, GenAI where appropriate)

Synthetic data generation, offline experimentation, and controlled realworld validation.

AI/ML platforms (Amazon SageMaker AI, Google Cloud Vertex AI, and Microsoft Azure AI)

Note: Working on Synthetic data experience is Mandatory

Please find the details below for one of our current urgent requirement. Please start sharing profiles ASAP.

Location - Chennai

Work Mode - Hybrid

Notice period - Immediate to 30 days

Interview process - Hackerrank + Technical Interview + Delivery Manager Discussion + Client interview

Data Scientist - 70%, AI/ML - 30%,

Domain - Healthcare & life sciences

About the role:

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 organization's 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 organization's 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 KPIsDelivery 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/riskPlatform, 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 selectionGovernance & 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 impactLeadership

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 organizationRequirements:

Master's 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

Robust 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 skillsDesirable:

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

📌 Data Scientist (Chennai)
🏢 Pyramid IT Consulting
📍 Chennai