Data Scientist

What you'll do

• Analyze engineering and manufacturing data to identify patterns and actionable insights.
• Develop predictive models to support quality assurance, process optimization, predictive maintenance, and product performance analysis.
• Partner with engineers, designers, and R&D teams to define data requirements and provide evidence-based recommendations.
• Build and maintain scalable data pipelines and machine learning models for real-time applications e.g., anomaly detection, fault prediction).
• Create digital twins, simulation models, or optimization algorithms to support engineering decision-making.
• Visualize complex engineering data and present findings to stakeholders in clear, actionable formats.
·        Design and implement modern, scalable data architectures to support AI/ML workloads.
·        Implement solutions integrating structured, semi-structured, and unstructured data.
• Ensure compliance with industry standards, data governance, and cybersecurity best practices.
• Stay current with emerging data science methods and their applications in engineering (e.g., Industry 4.0, AI in design, advanced materials).

Required Qualifictions

• Bachelor’s or Master’s degree in Data Science, Engineering, Computer Science, Statistics, or related field.
• Strong programming skills in Python, R, SQL; experience with MATLAB or C++ is a plus.
• Hands-on experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
• Familiarity with engineering software and data (e.g., CAD/CAE, IoT/SCADA systems, sensor data streams).
• Knowledge of predictive modeling, optimization algorithms, and statistical process control.
• Proficiency with Power BI
• Solid understanding of probability, statistics, and experimental design.

Preferred Qualifications

• Advanced degree (Master’s or Ph.D.) in Data Science, Engineering, or Applied Mathematics.
• Experience applying data science to predictive maintenance, supply chain optimization, or manufacturing automation.
• Familiarity with big data platforms (e.g., Spark, Hadoop) and cloud environments (AWS, Azure, GCP).
• Knowledge of digital twin technology, IoT analytics, or reliability engineering.
• Prior experience in mechanical, electrical, civil, or manufacturing engineering industries.
• Strong problem-solving skills with an engineering mindset.
• Ability to communicate complex data insights to engineers, operations staff, and leadership.
• Collaborative, with experience working on cross-disciplinary teams.
• Curiosity-driven and detail-oriented with a focus on continuous improvement.