Frost & Sullivan MetaBrain Early Careers Program – Data Science & Decision Intelligence Internship

Launch Your Career with Frost & Sullivan

At Frost & Sullivan, we believe that the future belongs to curious minds, innovative thinkers, and problem-solvers who are eager to make an impact. We are inviting applications from postgraduate students, recent graduates, and early-career professionals with up to two years of experience to join our growing global teams across various business, technology, research, consulting, AI, data, and corporate functions.

The Opportunity

Frost & Sullivan is looking for intern roles supporting MetaBrain. The work combines applied AI, business understanding, structured knowledge, quantitative models and trustworthy engineering to transform research and advisory into reusable software-enabled services. Build more than a demonstration. Work with industry researchers, advisors and engineers to turn AI capability into tested decision-intelligence software that enterprises can use.

Role Overview

Show how your technical work supports a business problem. Research, consulting or advisory experience is preferred but not mandatory. Academic projects, thesis and reproducible research implementations are valid evidence; internships do not require prior full-time employment.

Develop the quantitative models and data pipelines behind continuously updated advisory software. Combine rigorous statistical methods with a clear understanding of how enterprises prioritize growth opportunities and test alternatives.

Proposed engagement

Stipend: Yes, paid internship.

Duration: Preferably six months, possibility of an extension up to 12 months based on performance and where academic arrangements and work authorization permits are in place.

Full-time Conversion: Depends on assessed performance, a suitable vacancy and eligibility; it is not guaranteed.

Essential requirements

Currently pursuing or holding a Master's or PhD in AI, data science, statistics, econometrics, applied mathematics, operations research, computer science or a related field with substantive AI/ML coursework or research. Strong Python or R, SQL, probability, statistics and model validation. A reproducible project showing data preparation, a baseline and defensible evaluation.

Preferred: Time-series analysis, probabilistic modelling, optimization, causal inference, experimental design, survey methods or economic/market data. Familiarity with ML frameworks and version control is useful; candidates need depth in relevant methods rather than every technique.

Key responsibilities

Build reliable analytical datasets: Prepare authorized market, primary-research and enterprise data. Align time, geography, currency and business definitions; document transformations, missingness, sampling limitations and source lineage.

Develop decision models: Compare suitable approaches for forecasting, segmentation, ranking, opportunity scoring or optimization. Establish simple baselines before complex methods; explain the business meaning of targets, features and objective functions.

Test uncertainty and business value: Use appropriate held-out/time-based validation, sensitivity analysis and uncertainty estimates. Distinguish correlation, prediction and causality; avoid leakage and use explainability proportionate to the decision risk.

Operationalize the analysis: Create reproducible code, data tests and documented model interfaces. Build scenario outputs, drift/quality monitoring and visual explanations; integrate approved models with MetaBrain workflows through engineering review.