Senior Data Scientist

Role Overview

The Senior Data Scientist is tasked with creating, implementing, and managing sophisticated Machine Learning (ML) solutions tailored for industrial scenarios such as reliability analysis, predictive maintenance, and optimization of production processes. This role concentrates on scaling ML models that are production-ready and capable of delivering tangible business improvements within complex Oil & Gas sectors.

Key Responsibilities

• Architect and implement ML algorithms applicable to industrial contexts like monitoring asset reliability and forecasting maintenance needs.
• Develop models employing both supervised and unsupervised techniques, including regression and classification.
• Utilize advanced mathematical concepts such as linear algebra, calculus, probability, and statistics for the development and optimization of models.
• Create scalable ML frameworks using distributed computing methodologies including MapReduce and streaming technologies.
• Integrate industry-specific knowledge from Oil & Gas or Manufacturing sectors to formulate predictive and prescriptive analytics that are context sensitive.
• Work alongside data engineers and domain specialists to discover and validate pertinent data features.
• Convert operational goals and industrial requirements into effective ML and analytical solutions.
• Lead projects independently from defining problems to deploying and monitoring operational models.
• Rapidly prototype solutions using programming languages like Python, R, or JavaScript; knowledge of Java or Scala is considered advantageous.
• Implement MLOps strategies encompassing CI/CD pipelines, automated testing, version control, containerized deployment, monitoring, and diagnostics of performance drift.
• Guarantee that ML solutions are robust, explainable, and suitable for production deployment while respecting operational limitations.
• Effectively communicate complex technical findings to both technical teams and non-technical stakeholders.

Qualifications and Experience

• At least 10 years of direct experience in developing, deploying, and managing ML models in industrial scenarios related to reliability and predictive maintenance.
• Proven track record with Oil & Gas or Manufacturing industries, demonstrating strong domain knowledge.
• Hands-on experience with designing and executing ML systems in real industrial environments.
• Competency with scalable machine learning systems such as MapReduce and real-time streaming platforms.
• Experience in multidisciplinary collaboration involving engineers and technical experts.
• Proficiency in Python, R, or JavaScript; familiarity with Java or Scala is a plus.
• Experience in modern software development environments and AI-supported workflows.
• A Master’s degree in Computer Science, Electrical Engineering, Statistics, or related quantitative fields.
• Advanced knowledge and application of Machine Learning techniques including regression, classification, and both supervised and unsupervised learning approaches.
• Deep understanding of relevant mathematical foundations supporting ML model construction and validation.