Position Summary:

We are seeking a highly skilled and experienced Lead / Principal Data Scientist with a specialization in Machine Learning Engineering and Computer Vision to join our dynamic Data Science and AI team. In this pivotal role, you will be instrumental in transforming complex data from the oil and gas sector into actionable insights and innovative solutions that drive our business strategy forward. You will leverage advanced machine learning, deep learning, and neural network techniques, with a specific focus on asset health, anomaly detection, and computer vision applications, to solve complex business challenges within the oil and gas domain. You will collaborate closely with cross-functional teams to design, develop, and deploy scalable AI-driven models and algorithms. This position offers the opportunity to be at the forefront of AI/ML advancements, applying your expertise to real-world industrial analytics within the oil and gas sector.

Key Responsibilities:

• Solution Development: Lead the ideation, prototyping, and implementation of cutting-edge AI/ML solutions to meet emerging business requirements in the oil and gas industry.

• Machine Learning Pipelines: Design, develop, and implement end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment, specifically tailored for industrial analytics.

• Deep Learning Models: Develop, train, and optimize deep learning models using neural network architectures (e.g., CNNs, RNNs, Transformers, GANs) and frameworks such as TensorFlow, Keras, or PyTorch. Apply these models to solve complex problems in areas like computer vision, asset health monitoring, predictive maintenance, and operational optimization within oil and gas.

• Domain-Specific Modeling:

• Asset Health Modeling: Develop and deploy robust models for assessing and predicting the health of oil and gas assets.

• First Principle and Physics-Informed Modeling: Integrate first principle and physics-informed approaches with data-driven models to enhance predictive accuracy and interpretability in industrial applications.

• Statistical Analysis: Apply advanced statistical analysis techniques to identify patterns, quantify uncertainties, and support data-driven decision-making.

• Azure Databricks & Cloud Expertise: Architect, develop, and deploy scalable machine learning solutions utilizing Azure Databricks and other Azure cloud services for big data and ML solutions.

• Collaboration: Work closely with data architects, data engineers, and software developers to ensure effective data collection, processing, and storage. Collaborate with external partners, research institutions, and subject matter experts to gather domain-specific knowledge and datasets.

• Exploratory Data Analysis: Perform comprehensive exploratory data analysis to identify patterns, generate insights, and effectively communicate findings to technical and non-technical stakeholders within the oil and gas sector.

• Model Optimization: Optimize model performance by addressing issues such as overfitting, underfitting, and bias, ensuring scalability and performance in production environments.

• Mentorship: Mentor junior data scientists and ML engineers in model development, data handling, and ethical considerations in AI/ML practices, fostering a culture of continuous learning and excellence.

• Continuous Learning: Stay abreast of the latest advancements in machine learning, AI research, and computer vision techniques, actively applying new methods to enhance model performance and scalability in real-world industrial applications.

Desired Skills and Experience:

Education: Master’s or Ph.D. in Computer Science, Chemical Engineering, Statistics, Mathematics, or a related field with 7+ years of relevant experience, demonstrating a strong track record in AI/ML and computer vision. A Ph.D. is preferred.

Technical Proficiency: Expertise in Python and proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Proven experience with cloud platforms, specifically Azure, for deploying big data and ML solutions. Strong experience with Azure Databricks is essential.

Deep Learning Expertise: Extensive hands-on experience in applying deep learning techniques and neural networks to real-world problems, particularly in industrial analytics and computer vision. Proven ability to develop, train, and optimize models for high-impact computer vision applications such as image recognition, object detection, and multimodal modeling in an industrial context.

Domain Knowledge: Demonstrated domain knowledge and hands-on experience within the Oil & Gas industry, including understanding of operational data, asset types, and industry-specific challenges.

Analytical Skills: Superior problem-solving, critical thinking, and analytical capabilities. Experience with both relational and non-relational databases, time-series data, and advanced data visualization techniques.

Modeling Expertise: Strong experience in asset health modeling, first principle modeling, physics-informed modeling, and advanced statistical analysis.

Communication: Excellent verbal and written communication skills with the ability to convey complex technical concepts to non-technical stakeholders clearly and effectively, including senior leadership.

Collaboration: A strong team player mindset capable of leading and working collaboratively within diverse teams and contributing to an inclusive work environment.

Continuous Learning: A pronounced passion for continuous learning and staying ahead in AI/ML and computer vision fields, with enthusiasm for applying new technologies to solve real-world problems in the oil and gas sector.