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

Bachelors or Masters 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