Decision Intelligence Data Scientist for Real-Time Insights

Frost & Sullivan

Singapore

Sur place

SGD 90 000 - 150 000

Plein temps

Il y a 23 heures
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Résumé du poste

Frost & Sullivan and its MetaBrain initiative seek a data-driven analyst to turn market research and advisory insights into reusable software-enabled services. You will build data prep pipelines, statistical models and simulations that yield measurable, explainable outputs for decision support.

The role requires 3+ years in quantitative research or consulting, proficiency in Python or R and SQL, and a quantitative degree. Experience with econometrics and time-series methods is a plus.

Qualifications

  • 3+ years of strong quantitative research or consulting experience
  • Practical statistics, data preparation and model validation
  • Ability to demonstrate Python or R and SQL on reproducible analyses
  • Quantitative degree or equivalent applied capability
  • Econometrics, time-series methods, market sizing, survey weighting, optimization, experimental design, visualization, version control or cloud data workflows are valued

Responsabilités

  • Turn market research and advisory analysis into reproducible analytical services
  • Build data preparation, statistical models, opportunity scoring and simulations
  • Package analyses into reproducible notebooks, parameterized pipelines or services
  • Monitor data and model changes and explain results to advisors

Connaissances

Python
SQL
R
Statistics
Model validation

Formation

Quantitative degree

Outils

Jupyter

Description du poste

Frost & Sullivan and its MetaBrain initiative seek a data-driven analyst to turn market research and advisory insights into reusable software-enabled services. You will build data prep pipelines, statistical models and simulations that yield measurable, explainable outputs for decision support.

The role requires 3+ years in quantitative research or consulting, proficiency in Python or R and SQL, and a quantitative degree. Experience with econometrics and time-series methods is a plus.

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