Data Scientist

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Heinsohn

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1977

🏢 Enterprise

☁️ SaaS

👥 HR Tech

Enterprise • SaaS • HR Tech

Heinsohn is a Latin American technology company that provides enterprise software, cloud platforms, and IT services focused on digital transformation. The company develops custom software, web and mobile applications, AI and analytics solutions, and offers staff augmentation and SAP implementations. Heinsohn specializes in sector solutions for financial services, pensions, government and human resources—especially payroll and HCM systems—helping organizations automate operations, ensure regulatory compliance, and modernize legacy systems.

📋 Description

• Design and execute experiments to improve forecasting metrics across specific segments • Perform feature engineering on large-scale transactional data (~5M records in BigQuery) • Build user behavior models, including segmentation, propensity, churn, and retention • Evaluate models using strict temporal holdout methodologies and segment-level metrics • Document experiments with clear reasoning — hypotheses, results, and decisions • Collaborate with the ML Engineer responsible for maintaining the architecture and production pipelines

🎯 Requirements

• 3+ years of hands-on experience in Machine Learning — forecasting, supervised learning on user data, or both • Strong Python skills, including pandas, scikit-learn, XGBoost, statsmodels, and/or Prophet • Advanced SQL skills and experience working with cloud platforms (GCP is a plus) • Strong model evaluation judgment — you understand why random train/test splits are inappropriate for time series data • Experience performing feature engineering from transactional datasets • Ability to work within an existing framework and architecture without unnecessarily reinventing established solutions

🏖️ Benefits

• 100% remote work from any LATAM country • Opportunity to work on international projects with multicultural teams • Culture that encourages professional development and continuous learning • Spaces for innovation, well-being, and career growth