Tech Lead - Data Scientist

Purpose of the Role 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 Key Tasks & Activities 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 Accountability 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 Technical / Professional Requirements Bachelor's or Master's degree in Mathematics, Data Science, Statistics, Computer Science, or related field Strong theoretical knowledge in mathematical statistics and data science Practical experience in quantitative statistical analysis of large datasets Strong programming expertise in Python Experience developing scalable and performance-critical algorithms Hands-on experience with Python ecosystem tools: Jupyter, pandas, NumPy, SciPy, scikit-learn Experience with Apache Spark and Databricks is preferred Understanding of data pipelines, data lakes, and cloud-based analytics platforms Familiarity with CI/CD and automation practices in analytics workflows Knowledge of real-time or IIoT data processing is an advantage Personal Competencies 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) Performance Criteria 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