Data Scientist – Decision Intelligence

Frost & Sullivan

. Build decision-intelligence models, knowledge structures, evaluation assets, and governed workflows for Growth Pipeline Management and approved research/advisory use cases .

Posted 10/7/2026full-timeSingapore • SingaporeMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building decision-intelligence models and conducting quantitative research, with proficiency in Python, R, and SQL for data preparation and model validation. Capable of communicating complex statistical concepts and uncertainty clearly to stakeholders.

Highest-signal resume keywords

Python ProficiencyR ProficiencySQL ProficiencyModel ValidationQuantitative Research Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Decision-Intelligence ModelsData PreparationModel ValidationStatistical AnalysisForecasting ModelsSegmentation ModelsOptimization ModelsTime-Series MethodsExperimental DesignData Quality Tests

Soft Skills

Clear CommunicationAnalytical Thinking

Tools & Technologies

Reproducible NotebooksParameterized PipelinesCloud Data WorkflowsVersion Control

Certifications & Qualifications

Basic AI Certification

Industry Keywords

Market ResearchAdvisory AnalysisData ScienceEconometricsSurvey Weighting

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Build decision-intelligence models, knowledge structures, evaluation assets, and governed workflows for Growth Pipeline Management and approved research/advisory use cases
Turn market research and advisory analysis into reproducible analytical services
Prepare decision-ready data by combining authorized primary research, market sources, and enterprise inputs
Standardize units, time periods, and definitions; resolve duplicates, missing values, and outliers; retain source lineage and data-quality tests
Develop baselines and forecasting, segmentation, scoring, or optimization models
Document business targets, assumptions, and limits; select complexity based on validated performance and decision usefulness
Validate models using time-based or held-out testing; check sampling bias, leakage, and sensitivity
Quantify uncertainty where supportable and distinguish analyst scoring, statistical prediction, and causal inference
Package approved analyses into reproducible notebooks, parameterized pipelines, or services with engineering support
Monitor data and model changes, explain results to advisors, and connect scenarios to Advisory/Simulation outputs
Report to the Data Science Lead or Chief Architect – Technology, with a dotted line to the business solution lead

Requirements

What you’ll need

Typically 3+ years of strong quantitative research or consulting experience, with equivalent achievement considered
Practical statistics, data preparation, and model validation
Demonstrated Python or R and SQL proficiency on a reproducible analysis
Basic AI/ML learning and hands-on experimentation
A quantitative degree or equivalent applied capability is required
Preferred: econometrics, time-series methods, market sizing, survey weighting, optimization, experimental design, visualization, version control, or cloud data workflows
Domain expertise and clear communication of uncertainty are valuable
Basic AI certification alone does not qualify an applicant for independent data-science ownership
Strong researchers lacking coding or validation proficiency may enter as Decision Intelligence Analysts and progress after an assessed bridge
Production model approval remains with qualified reviewers