1.2. Methodology and Data Science

TalentUp’s platform is built on a rigorous, transparent, and scalable methodology that ensures the salary intelligence it delivers is accurate, consistent, and actionable. The combination of high-volume data collection, intelligent normalization, machine learning models, and human validation results in one of the most robust compensation benchmarking systems available.

Where Does the Data Come From?

TalentUp collects compensation data from a diverse mix of reliable sources, ensuring a comprehensive and representative view of the job market:

72% Job Boards
The majority of TalentUp’s data comes from public job portals such as LinkedIn, Indeed, Monster, InfoJobs, Adzuna, and hundreds of others. These listings are scraped daily to capture job descriptions, salary offers, location, benefits, and company metadata—creating a rich pool of real-time market information.

17% Employee-Submitted Profiles on TalentUp.io
Employees can submit their compensation details through the TalentUp platform. These submissions are carefully validated by comparing them against similar job roles, companies, and locations, ensuring the data is accurate and relevant.

11% HR Datasets from Client Companies
TalentUp clients can upload anonymized internal compensation data via Excel templates or integrations with HR platforms like Personio and BambooHR. This data undergoes thorough review and is only included if it was updated in the current year, enhancing both accuracy and freshness.

4-Step Data Process

To ensure data integrity and usability, TalentUp follows a four-step methodology: Collection, Normalization, Deduplication, and Validation.

TalentUp collects over 20,000 new salaries per day from 300+ sources across 70+ countries and 600+ job roles. This large-scale intake provides the foundation for both volume and geographic diversity. In addition to salaries, other data points include bonuses, job descriptions, responsibilities, company information, and benefits.

Once collected, all data is standardized to allow meaningful comparison across different geographies and company types. This includes:

Currency Conversion: Salaries from international sources are automatically converted based on daily exchange rates to ensure consistency.

Job Title Translation: TalentUp’s taxonomy includes over 32,000 mapped roles in multiple languages, ensuring job titles are interpreted uniformly.

Benefit Standardization: Common benefits (e.g., health insurance, remote work, stock options) are interpreted and normalized, even when described differently across sources.

This process ensures that a “Software Engineer” in São Paulo can be fairly compared with the same role in Berlin or Toronto, regardless of how the data was originally submitted.

To maintain the integrity of the dataset, duplicate listings and repeated job offers are filtered out using natural language processing (NLP) algorithms. These algorithms detect semantic duplicates and similarities in job descriptions and employer metadata. This step ensures every data point is unique, reducing noise and inflating values.

TalentUp applies both automated and manual validation processes to guarantee reliability:

Benchmark Comparison: If there are sudden changes in benchmark values (e.g., a 20% salary increase in a city-role combo), the system flags the anomaly for further review.

Sample Size Threshold: A minimum of 30 samples per position and location is required to build a benchmark. This ensures statistical reliability in the data.

Manual Cross-Check: If the data significantly deviates from historical benchmarks or industry standards, TalentUp’s team performs a manual review and cross-verification with partner sources.

All benchmarks are refreshed every 1–2 months, keeping the platform current and dependable.

Predictive Modeling

When data is sparse for a specific role, city, or level of seniority, TalentUp uses predictive analytics to fill the gaps—ensuring continuity in insights without compromising reliability.

Linear Regression Models
TalentUp applies linear regression models to estimate salary trends based on seniority and experience. These models are fine-tuned to reflect realistic compensation growth over time, ensuring that predicted values align with expected career progression.

Correlation Across Similar Markets
If direct data is unavailable for a given city, TalentUp predicts salary benchmarks by leveraging correlations with similar locations, industries, or company sizes. This allows for salary forecasting even in less saturated data regions.

Data Completion
Predictive modeling allows TalentUp to offer comprehensive salary ranges—from base pay to bonuses—even when only partial data is available. This enhances the usability of the platform and ensures a consistent experience across all roles and locations.

SummaryTalentUp’s methodology balances big data scale with data science precision and human validation. Every data point is vetted through a transparent and structured process, making TalentUp a trusted source for HR and compensation professionals worldwide. Whether you’re benchmarking current employees, analyzing the market, or planning for the future, you can count on the platform’s methodology to deliver insights you can act on with confidence.

1. Introduction

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