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

Posted 21 Days Ago
Be an Early Applicant
In-Office
Singapore, SGP
Internship
In-Office
Singapore, SGP
Internship
Develop quantitative models and data pipelines for MetaBrain decision-intelligence software. Responsibilities include preparing reliable datasets, building forecasting, segmentation, ranking, scoring, or optimization models, validating uncertainty and business value, and operationalizing reproducible analytical workflows with data tests, monitoring, scenario outputs, and explainability.
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Launch Your Career with Frost & Sullivan

MetaBrain combines Frost & Sullivan’s 65 years of industry expertise, enterprise data, and third-party insights, delivering human-reinforced AI insights and analytics to solve your toughest business use cases with actionable, real-time intelligence. Learn more at https://metafrost.ai/

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.

Frost & Sullivan Singapore, Singapore, SGP Office

Singapore, Singapore

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