Data Scientist

Job Purpose

TechnipFMC leads the transformation of the energy industry by transforming our clients’ project economics through fully integrated projects, products, and services. Making robust decisions efficiently and consistently by using data about our products, processes, and operations is a key competency for our business to achieve our true north.

In this context, the business is developing its Advanced Analytics capability with the aim of better leveraging our data to deliver new insights, value and smarter ways of working across our value stream. Data Science is a key discipline in this context that uses statistical analysis, machine learning, and predictive modeling techniques to generate actionable insights from data, driving informed decision-making and innovation.

This role is for a Data Scientist who will be a member of the Advanced Analytics team (within Software Services) that is responsible for developing the company’s data analytics strategy and roadmap.

Job Description

People & Teams:

You are meant for this job if:

• Minimum of 5 years of experience in data science, developing advanced solutions using state-of-the-art machine learning techniques.
• Applying machine learning to develop solutions for the oil and gas industry.
• Collaborating with business and technical stakeholders to deliver tailored solutions.
• Manage delivery of data science project milestones, ensuring on-time & on-quality delivery.
• Ability to evaluate and direct technical work performed by junior data scientists.

• Advanced – Programming in Python (preferred), R, SQL, and Scala.
   
• Advanced – Proficient use of core data science libraries such as scikit-learn, NumPy, and Pandas.
   
• Advanced – Designing and applying machine learning models for diverse data types including tabular, unstructured (e.g., text, images), and time-series data.
   
• Advanced – Strong foundation in statistical methods and their practical application in data science workflows.
   
• Advanced – Data visualization using tools such as Matplotlib, Seaborn, Power BI, and Tableau.
   
• Advanced – Using version control systems like Git for collaborative development and code management.
   
• Proficient – Familiarity with generative AI, foundation models, and LLMs (Large Language Models) to stay aligned with emerging trends in data science.
   
• Proficient – Managing the machine learning lifecycle using tools like MLflow, Docker, and Kubernetes.
   
• Proficient – Applying advanced techniques in Natural Language Processing (NLP), Deep Learning, and leveraging AutoML for efficient model development and deployment.
   
• Proficient – Working with SQL and NoSQL databases including MySQL, PostgreSQL, MongoDB, and Cassandra.
   
• Proficient – Applying structured problem-solving and continuous improvement methodologies such as Plan-Do-Check-Act (PDCA).
   
• Proficient – Practicing Agile development using tools like JIRA or Trello.
   
• Competent – Managing production-grade ML solutions on cloud platforms using MLOps practices.
   
• Competent – Implementing data security and compliance practices, especially relevant to regulated industries like oil and gas.
   
• Competent – Ensuring code quality using tools like SonarQube and adhering to software engineering best practices.
   
• Competent – Understanding the software development lifecycle (SDLC) and its integration with data science workflows.
   
• Competent – Working with high-performance ML frameworks such as TensorFlow and PyTorch.
  
  
  

Skills