Expert Data Scientist (Pune)

Ciklum is looking for an Expert Data Scientist to join our team full-time in India. We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live. 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 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: - 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 - 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 Whats in it for you - Strong community: Work alongside top professionals in a friendly, open-door environment - Growth focus: Take on large-scale projects with a global impact and expand your expertise - Tailored learning: Boost your skills with internal events