Decision Scientist

Job not on LinkedIn

🔥 2 hours ago

🌐 Colombia, Argentina, +4 more countries – Remote

🇨🇴 Colombia – Remote 🇦🇷 Argentina – Remote 🇵🇦 Panama – Remote 🇨🇱 Chile – Remote 🇧🇷 Brazil – Remote 🇲🇽 Mexico – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

✨ Scientist

👻 Ghost score 12%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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CRNCY (BZ) Limited

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🤝 B2B

💸 Finance

Consulting • B2B • Finance

CRNCY (BZ) Limited, pronounced Currency, is an investment holding company focused on building top tier businesses across medium-sized less competitive markets within the Commonwealth group of countries. Their mission is to develop the most efficient, well-run, and profitable companies within their management portfolio, specializing in business development, project management, information technology, and financial services.

📋 Description

• Analyze portfolio performance and identify opportunities to improve profitability through credit strategy • Model probability of repayment, default, and fraud from incomplete and alternative data • Build the statistical inference behind credit-risk models • Translate analysis into credit rules, including approval strategy, underwriting requirements, first-loan sizing, and customer segmentation • Optimize underwriting requirements and reduce unnecessary customer friction • Design and assess experiments to improve lending outcomes • Quantify trade-offs among growth, risk, customer experience, and profitability • Translate analysis into practical underwriting and portfolio recommendations • Frame questions, build analyses, and own resulting decisions without delegating to an analytics team • Work directly with credit and finance leadership and the founder • Own decisions across multiple markets

🎯 Requirements

• Experience using data and probabilistic reasoning to make or influence important business decisions • Experience evaluating trade-offs between competing objectives under uncertainty • Experience applying analytical judgment rather than relying solely on predefined rules or models • Experience translating analysis into practical business decisions and recommendations • Accountability for business outcomes of decisions • Experience in portfolio strategy or analytics, underwriting strategy, lending strategy, risk management, or another analytical role involving high-consequence decision-making under uncertainty • Degree from a strong university in Decision Science, Operations Research, Applied Mathematics, Statistics, Economics, Engineering, or Computer Science with significant quantitative coursework • Experience with decision science, risk optimization, lending strategy, or portfolio economics • Experience with customer segmentation, expected value analysis, risk-adjusted returns, or pricing optimization • Experience with credit risk, underwriting analytics, scorecards, probability of default, first-payment default, expected loss, or repayment behavior analysis • Experience with insurance-related risk work such as actuarial pricing, underwriting analytics, risk selection, loss forecasting, claims analytics, fraud detection, or risk-based pricing • Experience with alternative, behavioral, or unstructured data; thin-file customer environments; cohort analysis; vintage analysis; expected loss; customer lifetime value; or portfolio performance tracking • Experience with experimentation, causal inference, A/B testing, champion/challenger testing, Bayesian testing, or Monte Carlo simulation • Experience using messy internal data to improve real business decisions • SQL and Python at an analysis-and-modeling level, including writing original code • Knowledge of probability, statistics, segmentation, predictive modeling, and expected-value analysis • Knowledge of logistic regression, scorecards, XGBoost, LightGBM, or similar practical models • Knowledge of backtesting, out-of-time validation, experiment design and power analysis, and validation discipline including data leakage prevention • Fluent English required • Meaningful daily overlap with the Americas required

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