Data Scientist - On-Site San Pedro Sula

About the role

A U.S.-based company is looking for a Data Scientist to join its data science and analytics team. The role involves analyzing complex datasets, building predictive models and applying machine learning to turn data into actionable business insights. It is an on-site position in San Pedro Sula, supported by the recruiter while working as part of the client's team.

Key responsibilities

• Develop and deploy predictive models using machine learning techniques.
• Analyze complex datasets to identify trends and business insights.
• Perform data cleaning, transformation and feature engineering.
• Collaborate with stakeholders to define analytical requirements.
• Monitor and optimize model performance and accuracy.
• Ensure compliance with data privacy and security standards.
• Integrate analytical models into production environments.
• Create dashboards, visualizations and analytical reports.
• Maintain technical documentation and troubleshoot data-related issues.

Required profile

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics or a related field.
• 3+ years of experience in data science, data analytics or statistical modeling.
• Strong knowledge of statistical analysis and predictive modeling.
• Experience with large datasets and data processing pipelines.
• Familiarity with Tableau, Power BI or similar visualization tools.
• Knowledge of data privacy regulations such as GDPR and CCPA.
• Understanding of Git and collaborative development environments.
• Strong written and spoken English.

Required skills

• Proficiency in Python, R and SQL.
• Machine learning frameworks such as Scikit-learn, TensorFlow or PyTorch.
• Big data technologies such as Apache Spark or Hadoop.

What we offer

• Competitive salary and annual performance reviews.
• Private medical insurance and meal subsidy.
• Permanent employment contract and paid time off and holidays.
• Training and development programs, recognition programs and a modern work environment.
• Weekends off.