Senior Data Scientist

About the Role

Key Responsibilities

• Lead the design, development, and implementation of AI/ML solutions for applications specific to oil and gas development and operations.
• Architect end-to-end ML pipelines that address the full lifecycle of AI/ML solutions from ideation to production deployment, ensuring scalability and business impact.
• Collaborate with multi-functional teams including geologists, petroleum engineers, and operations staff to translate complex business problems into effective data-driven solutions.
• Provide technical leadership across AI/ML projects, establishing best practices and mentoring junior team members to build organizational capability.
• Drive MLOps best practices, including CI/CD pipelines, model monitoring, and automated retraining workflows.
• Spearhead research initiatives to solve complex business problems using statistics, ML, and foundation models, serving as the SME in advanced ML techniques.
• Identify high-impact AI opportunities, prioritizing initiatives that drive revenue growth and operational efficiency.
• Partner with Data Engineering to design scalable data pipelines for ML consumption to ensure real-time data integrations and develop model-serving architectures.
• Apply predictive modeling, LLMs, and deep learning to extract actionable insights from large-scale datasets.
• Leverage data science tools and techniques in analyzing large datasets to develop custom models and algorithms for uncovering insights, trends, and patterns.
• Evaluate analytics and machine learning technologies for business use and communicate findings through reports and presentations.
• Partner with non-technical departments to demonstrate how data science can benefit their effectiveness and performance.
• Stay ahead of AI advancements such as Generative AI and reinforcement learning and assess their business viability.

Requirements

• A master's degree (PhD preferred) in Statistics, Machine Learning, Mathematics, Computer Science, Economics, or related quantitative field.
• 5+ years of experience in machine learning, including supervised, unsupervised, and ensemble methods, with deep learning experience as a bonus.
• 3+ years of experience developing and deploying machine learning models in production environments with demonstrable impact.
• Proficiency in Python and relevant data science/ML libraries including TensorFlow, PyTorch, scikit-learn, and pandas.
• Hands-on experience with cloud computing platforms such as AWS, Azure, or GCP.
• Experience with industrial data platforms and large-scale data processing tools like Spark/PySpark.
• Solid understanding of statistical analysis, experimental design, and data preprocessing techniques for industrial applications.
• Knowledge of DevOps practices and CI/CD pipelines for ML model deployment.
• Ability to design fault-tolerant ML systems with monitoring and automated retraining pipelines.
• Demonstrated ability to conduct rapid Proof of Concepts (POCs) using design thinking methodologies.
• Exposure to modern ML libraries such as Hugging Face Transformers, LangChain, and LlamaIndex.

Nice to Have

• Ability to lead others in drawing conclusions from data and recommending actions.
• Ability to succeed in a fast-paced environment and deliver high-quality performance on multiple tasks.
• Relentless drive, determination, and self-learning ability.
• Highly organized and attentive to detail.

Benefits & Perks

• Range of supplemental benefits including medical, dental, vision, life, disability, and other insurance plans.
• Employee stock purchase program (ESPP) and 401K with company match after 12 months.
• Health Savings Account (HSA) on the HDHP plan.
• SupportLinc Employee Assistance Program (EAP) with up to 8 free counseling sessions.
• Corporate discount savings program and other discounts.
• On-demand training, certification prep, and access to technical and leadership courses after 6+ months.
• Certification discounts and perks for professional associations.
• Dedicated customer service team and certified Career Coach.