Our client is a global leader in the energy sector, specialising in both subsea and surface technologies. Their mission is to enhance the performance of the world’s energy industry. They achieve this by constantly challenging conventions and investing in over 20,000 employees across 48 countries. The company strives to provide an inspiring work environment, tackling some of the world's most complex technical and engineering challenges in collaboration with a truly global team.

Requirements

• 6+ years of experience in applied data science roles
• Proficiency in Python and ML libraries (scikit-learn, TensorFlow, Keras)
• Proficient use of core data science libraries such as scikit-learn, NumPy, and Pandas
• Experience with data visualization tools (Matplotlib, Seaborn, Power BI, or Tableau)
• Experience with SQL and NoSQL databases, including MySQL, PostgreSQL, MongoDB, and Cassandra
• Experience with generative AI, foundation models, and LLMs
• Experience with advanced techniques in Natural Language Processing (NLP), Deep Learning, and leveraging AutoML
• Experience with statistical methods in the data science workflow
• Experience with applying machine learning to develop solutions for the oil and gas industry
• Designing and applying machine learning models for diverse data types, including tabular, unstructured (e.g., text, images), and time-series data
• Excellent communication skills and the ability to translate complex data into actionable business insights.
• Ability to evaluate and direct technical work performed by junior data scientists

Nice to have:

• Experience in R, SQL, or Scala

English level Fluent

Responsibilities:

• Develop data-driven solutions using machine learning to provide useful insights and solve complex challenges in the oil and gas sector
• Work with various machine learning and data science methods (unsupervised learning, deep learning, NLP, and time series analysis) and manage the machine learning lifecycle using tools like MLflow, Docker, and Kubernetes
• Implementing data security and compliance practices, especially relevant to regulated industries like oil and gas
• Applying structured problem-solving and continuous improvement methodologies such as Plan-Do-Check-Act (PDCA)
• Perform exploratory data analysis (EDA) to uncover trends, patterns, and key findings that guide model design.
• Use industry knowledge to choose the most suitable analytical approaches.
• Build reliable models through careful testing, validation, and performance checks.
• Help deploy and maintain machine learning models in production systems.
• Present insights and results clearly to both technical teams and non-technical audiences
• Ensuring code quality using tools like SonarQube and adhering to software engineering best practices
• Drive innovation by mentoring junior scientists and introducing cutting-edge techniques and frameworks

Location - remote from Bulgaria, Lithuania, Poland, Romania