Skills-First Hiring

Through Real Job Simulations

Welcome to Talent360Global where we believe hiring should start with skills, not assumptions. Traditional hiring often relies on CV keywords, degrees, and automated filters that rarely show what a person can actually do. Our Skills‑First approach changes this by allowing candidates to demonstrate real capability through structured job simulations.

Talent360Global’s simulations are built on the Skills Narrative framework, a model that records verifiable evidence of capability rather than relying on self‑reported credentials. Instead of being filtered out by opaque algorithms, candidates complete practical simulations based on real job profiles such as AI/ML Engineer, Data Analyst, HR Analytics Specialist, Finance Analyst or hotelier (open to all fields). These simulations typically take up to max. eight hours and are designed to reflect realistic tasks and decision‑making scenarios.

Our evaluation uses Explainable AI (XAI) together with human oversight. AI is used to validate evidence, benchmark performance, and document outcomes transparently not simply to reject candidates. Each simulation generates a structured skills record and an auditable explanation of results, ensuring that both candidates and hiring organizations clearly understand the evaluation outcome. This transparency addresses a major weakness of traditional AI‑driven résumé screening systems, where decisions often remain opaque and difficult to challenge.

For candidates, the goal is simple: prove what you can do. Successful participants may have their profiles shared with companies actively hiring for similar roles. This approach saves time for employers and reduces hiring cycles while giving candidates a fairer opportunity to demonstrate capability.

The Skills Narrative framework also supports reskilling and upskilling. If your current profile does not match market demand, we help you restructure and present your capabilities based on real job requirements rather than assumptions. By aligning skills with actual market opportunities, candidates can reposition themselves more efficiently and move toward roles that truly fit their capabilities.

At Talent360Global, hiring is no longer just about screening CVs. It is about verifying capability, improving transparency, and connecting talent with real opportunities faster and more fairly.

Professions & Role Simulations

Participation Notice

Email your CV to HRMservices@talent360global.de to participate. Qualified candidates will receive the simulation access link by email.

By taking part in this simulation, you confirm that you are seeking employment in this role. Successful profiles may be shared with companies hiring for similar positions. Participation implies consent for Talent360Global to process and share your profile for recruitment purposes.

AI/ML Engineer

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Finance

About the Role (AI/ML Engineering Simulation)

Talent360Global invites engineers and data professionals worldwide to participate in a skills-based simulation for the role of AI / Machine Learning Engineer. This simulation is based on real market vacancies and is designed to identify professionals who can demonstrate practical machine learning capability through real problem solving.

This is not a job offer.

Candidates who successfully complete the simulation will have their profiles shared with partner organizations and companies actively hiring for AI / Machine Learning Engineer roles, participation certificate, and placement in our database of qualified candidates

Responsibilities

Design and implement machine learning models and algorithms to solve data-driven problems
Analyze and preprocess structured or unstructured datasets
Perform feature engineering and select appropriate data representation methods
Train, evaluate, and optimize machine learning models
Conduct model testing and experiments to improve performance
Build data pipelines and prepare datasets for machine learning workflows
Collaborate with engineering or data teams to translate business problems into machine learning solutions
Deploy machine learning models or propose deployment architecture for production systems
Monitor model performance and improve prediction accuracy

Skills

Strong programming ability (Python preferred)
Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
Knowledge of machine learning algorithms, classification, regression, and model evaluation
Experience with data analysis libraries such as Pandas and NumPy
Understanding of statistics, probability, and data modeling
Ability to analyze large datasets and extract insights
Familiarity with cloud infrastructure, APIs, or model deployment pipelines is an advantage
Strong analytical and problem-solving skills
Ability to document and communicate technical approaches clearly

Qualifications & Experience

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, or a related technical field
Typically 3–5 years of experience in machine learning, AI, or data science roles
Experience building machine learning models or data-driven systems
Familiarity with machine learning experimentation and model evaluation techniques

Simulation Overview

Successful candidates will receive a link to complete a structured technical simulation designed to reflect real machine learning engineering tasks.

The simulation is completed remotely and has a maximum duration of 8 hours.

What to Submit

Curriculum Vitae (CV)
Educational documents
Reference letter from previous employer (if available)
Optional proof of previous employment or project experience (We recommend to request references from your managers and colleagues while being on the job)

Onboarding

Simulation onboarding will take place online via our LinkedIn event on 29 March 2026 from 13:00 to 15:00 (Berlin Time), Please adjust for your local time zone.

Participants should register here: https://www.linkedin.com/events/7428171353423233024

Participation is free for simulation candidates.

The final access link and instructions will be shared two day before the event on 27 March 2026.

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