Senior Data Scientist

Overview

Senior Data Scientist role at Apex Systems.

Type: Direct Hire

Location: Houston, Texas or Denver, Colorado

Schedule: Alternating, 1 week onsite and 1 week remote. (50% remote and 50% onsite)

• 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, while serving as the SME in advanced ML techniques, educating partners on model capabilities and limitations
• 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 that will enable development of custom models and algorithms to uncover insights, trends, and patterns in the data
• Responsible for the evaluation of analytics and machine learning technologies for use in the business and communicates findings to key partners through reports and presentations
• Partners with other non-technical departments within the business to assist them in understanding how data science can benefit them and improve their effectiveness and performance
• Stay ahead of cutting-edge AI advancements (e.g., Generative AI, reinforcement learning) and assess their business viability

• 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 (supervised, unsupervised, and ensemble methods), natural language processing; deep learning experience is a bonus
• 3+ years of experience developing and deploying machine learning models in production environments with demonstrable impact to the business
• Demonstrated expertise in Python and ML libraries including Tensor Flow, PyTorch, scikit-learn, and pandas; ability to create visualizations and tell persuasive data stories
• Hands-on experience with cloud computing platforms (AWS, Azure, GCP) and proficiency with industrial data platforms
• Proven track record of developing, scaling, and implementing models in customer-facing environments
• Solid understanding of statistical analysis, experimental design, and data preprocessing techniques for industrial applications
• Knowledge of Dev Ops practices and CI/CD pipelines for seamless ML model deployment in production environments
• Proven ability to design fault-tolerant ML systems with monitoring and automated retraining pipelines, along with model-serving and event-driven architectures
• Demonstrated ability to conduct rapid Proof of Concepts (POCs) using design thinking methodologies
• Exposure to modern ML libraries (Hugging Face Transformers, Lang Chain, Llama Index), Spark/PySpark for large-scale data processing

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

Apex Systems is an equal opportunity employer. We do not discriminate or allow discrimination on the basis of race, color, religion, creed, sex, age, sexual orientation, gender identity, national origin, ancestry, citizenship, genetic information, disability, veteran status, or any other characteristic protected by law.