Data Science Engineer

Data Science Engineer

Job Overview

The Data Science Engineer is responsible for designing, building, and optimizing data pipelines, machine learning models, and analytics solutions that support data-driven business decisions. This role bridges data engineering and data science, transforming raw data into scalable, production-ready AI and analytics solutions. The ideal candidate can work independently, solve complex data challenges, and collaborate with cross-functional teams.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL workflows for structured and unstructured data
• Build, train, deploy, and maintain machine learning and AI models in production environments
• Collect, clean, transform, and analyze large datasets to generate actionable insights
• Work closely with business stakeholders, product teams, and engineers to understand data requirements and translate them into technical solutions
• Optimize databases, data warehouses, and data infrastructure for performance, scalability, and reliability
• Ensure data quality, integrity, governance, and security across systems
• Monitor, evaluate, and improve the performance of machine learning models and data workflows
• Create dashboards, reports, and visualizations to support business intelligence and decision-making
• Automate data workflows and improve operational efficiency through scripting and process optimization
• Troubleshoot data issues and provide technical recommendations for process improvements
• Mentor junior team members and contribute to best practices in data engineering and data science

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field
• 3-7 years of professional experience in data science, data engineering, machine learning, or related roles
• Strong hands-on experience in Python and SQL for data processing, analytics, and automation
• Experience working with Oracle DB or other enterprise relational databases
• Solid understanding of machine learning algorithms, statistical analysis, and predictive modeling
• Experience designing and maintaining ETL pipelines and data architectures
• Experience deploying ML models into production environments
• Familiarity with cloud-based data and ML services
• Strong analytical, problem-solving, and debugging skills
• Effective communication skills with the ability to collaborate with technical and non-technical stakeholders
• Ability to work independently, manage multiple priorities, and deliver high-quality solutions.