Tech Lead - Data Scientist

· Design and develop advanced data science and analytics solutions for various IIOT platforms

· Enable data-driven decision-making through statistical analysis, predictive models, and scalable analytical algorithms

· Transform large-scale industrial and telemetry data into actionable business insights

· Support long-term AI/ML and analytics strategy within TPD digital initiatives

· Develop scalable analytical models and performance-critical algorithms using Python

· Perform quantitative statistical analysis on large-scale industrial and telemetry datasets

· Design and implement data science workflows using Python, Spark, and Databricks

· Build predictive analytics and data-driven insights for PumpTest and IIoT solutions

· Collaborate with engineering, analytics, and product teams on data-driven use cases

· Contribute to architecture decisions for analytics and data science platforms

· Ensure reliability, maintainability, and performance of analytical solutions

· Support data modeling, transformation, and feature engineering processes

· Drive automation, monitoring, and continuous improvement of analytical workflows

· Contribute to reusable frameworks and best practices for data science initiatives

· Own development and quality of analytical models and algorithms

· Ensure scalability and accuracy of data science solutions

· Support business decision-making through reliable insights and predictive analytics

· Drive alignment between business needs and data-driven solutions

· Contribute to long-term analytics and AI/ML platform strategy

· Disciplined and sustainable coding practices

· Strong analytical and problem-solving skills

· Precise, structured, and detail-oriented working style

· Self-motivated with strong learning agility and hands-on mentality

· Strong communication and stakeholder collaboration skills

· Team-oriented mindset and ability to work cross-functionally

· Very good English communication skills (written and spoken)

· Accuracy and reliability of analytical models

· Scalability and performance of data science solutions

· Timely delivery of analytics initiatives

· Quality and maintainability of code and algorithms

· Business value generated through insights and predictive analytics

· Collaboration effectiveness across teams