Analytical Engineer / Data Scientist

Job Description Roles & Responsibilities We are seeking a motivated Analytical Engineer / Data Scientist with an engineering mindset to join our technical team in Armenia. This role supports analytics, engineering analysis, predictive modelling, and data-driven decision support initiatives across engineering and operations use cases. You ll work on data-driven and analytics-enabled solutions, translating raw operational, technical, and business data into actionable insights and deployable models. We re looking for someone who thinks like an engineer first, and who also brings a strong data science and analytics skillset to that thinking, comfortable with engineering-style data, diagrams, process/equipment data, and technical documentation, as well as sensor/telemetry data and statistical modelling. Familiarity with Oil & Gas or Mining is a plus but not required; strong candidates from other engineering-adjacent backgrounds are welcome. This position reports to the Engineering Manager/Data Science Lead and works closely with a peer Data Scientist, sharing coverage of a technical scope previously held by a single senior team member. Responsibilities / Duties: Conduct engineering analysis on equipment, process, and operational performance, applying engineering principles and calculations alongside data-driven methods. Develop, train, validate, and deploy analytical, statistical, and machine learning models to support business and engineering use cases. Perform data exploration, feature engineering, and statistical analysis on structured and semi-structured datasets, including time-series, sensor, or technical/engineering data where applicable. Collaborate with engineering, operations, and digital teams to integrate models into production environments. Design and maintain data pipelines to ensure reliable, high-quality data ingestion and processing. Conduct quality assurance on data extraction and model outputs, and clearly communicate data accuracy and completeness to technical and non-technical stakeholders. Monitor model performance and continuously improve accuracy, robustness, and interpretability. Troubleshoot issues in data pipelines and model behavior; evaluate new tools, techniques, and technologies as they emerge. Maintain clear documentation of model development, data processing logic, and revisions. Partner with the Engineering Manager/Data Science Lead and peer Data Scientist to divide and cover technical scope. Stay up to date with emerging trends and best practices in data science, engineering analytics, and applied statistics. Desired Candidate Profile Bachelor s or Master s degree in Engineering (Mechanical, Petroleum, Mining, Industrial, Chemical, Electrical, or similar), Data Science, Computer Science, or a related field. Engineering degrees strongly preferred. Hands-on experience applying engineering principles or calculations to analyze equipment, process, or operational performance. 2 5 years of professional experience in engineering analysis, data science, machine learning, or applied analytics roles. Strong proficiency in Python (required). Solid understanding of statistics, probability, and machine learning algorithms. Experience working with relational databases. Familiarity with data visualization tools and techniques to present insights effectively. Comfort working with operational, technical, or industrial data (equipment, process, sensor, or engineering documentation) from any industry. Strong attention to detail, particularly when validating data quality or model output accuracy. Ability to work independently while collaborating effectively within cross-functional teams, and to partner closely with a peer data scientist on shared scope. Fluent written and spoken English. Preferred / Additional Skills Experience with cloud platforms (AWS, Azure, or GCP) or containerization tools (Docker). Understanding of MLOps concepts, model versioning, and deployment pipelines. Knowledge of software engineering best practices (Git, code reviews, unit testing). Experience with time-series analysis, predictive maintenance, reliability modeling, or geospatial/GIS data. Experience with document/image-based data extraction (e.g., PDFs, scanned documents, AutoCAD, or engineering diagrams such as P&IDs/isometrics) is a strong plus. Experience performing QA on data-driven or audit-style workflows. Experience working in industrial, engineering, or operations-focused environments Oil & Gas or Mining a plus, not required. Company Industry Oil & GasPetroleum Department / Functional Area Engineering Keywords Analytical Engineer / Data Scientist Get real-time job updates only on our App