Frost MetaBrain: Data Scientist – Decision Intelligence

Bring your research and consulting expertise into MetaBrain solution development. Turn industry knowledge and advisory methods into reusable, continuously updated software-enabled services.

MetaBraincombines Frost & Sullivan’s 60 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.

https://metafrost.ai/

The work

Selected colleagues will build decision-intelligence models, knowledge structures, evaluation assets and governed workflows for Growth Pipeline Management (GPM) and other approved research/advisory use cases.

The ambition extends beyond generating reports or building a generic chatbot: create configurable solutions that help clients identify opportunities, evaluate alternatives, simulate scenarios and monitor change, with accountable advisor review.

Key responsibilities

Reporting to Data Science Lead or Chief Architect - Technology, with dotted line to the business solution lead, the incumbent will be responsible to turn market research and advisory analysis into reproducible analytical services. Build the data preparation, statistical models, opportunity scoring and simulations that make MetaBrain outputs measurable, explainable and continuously refreshable.

• Prepare decision-ready data. Combine 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.
• Build and compare models. Develop baselines and suitable forecasting, segmentation, scoring or optimization models. Document the business target, assumptions and limits; select complexity only where it improves validated performance or decision usefulness.
• Validate and simulate. Use suitable time-based or held-out testing; check sampling bias, leakage and sensitivity. Quantify uncertainty where supportable; distinguish analyst scoring, statistical prediction and causal inference.
• Make analysis reusable. 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.

Essential requirements

• A strong quantitative research or consulting record; typically 3+ years, with equivalent achievement considered. Practical statistics, data preparation and model validation.
• Ability to demonstrate Python or R and SQL on a reproducible analysis, plus 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.
• Entry level and authority: Basic AI certification alone does not qualify an applicant for independent data-science ownership. Strong researchers who lack coding or validation proficiency may enter as Decision Intelligence Analysts and progress after an assessed bridge; production model approval stays with qualified reviewers.