Marketing Data Scientist

Data Scientist
Hybrid Offshore/Onshore: TechnipFMC is committed to driving real change in the energy industry. Our ambition is to build a sustainable future through relentless innovation and global collaboration – and we want you to be part of it. You'll be joining a culture that values curiosity, expertise, and ideas as well as equal opportunities, inclusion, and authenticity. Bring your unique energy to our team of more than 21,000 people worldwide, and discover a rewarding, fulfilling, and varied career that you can take in anywhere you want to go.
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 Develop data analytics solutions using machine learning algorithms to generate actionable insights for solving complex problems in the oil and gas industry.
Leverage expertise in machine learning theory to implement tailored solutions for specific business challenges and datasets.
Utilize a diverse range of machine learning algorithms and data science technologies - including supervised and unsupervised learning, deep learning, natural language processing (NLP), and time series analysis - to address varied business challenges.
Ensure data quality through preprocessing and cleaning to prepare datasets for analysis.
Conduct exploratory data analysis (EDA) to identify patterns, anomalies, and insights that inform model development.
Build robust solutions through rigorous testing, verification, and validation.
Develop and track performance metrics to evaluate the impact of data science solutions.
Manage the delivery of data science project milestones.
Ensure all solutions comply with ethical standards, including data privacy and bias mitigation.
Support the deployment and monitoring of machine learning models in production environments.
Stay current with advancements in machine learning and apply them to enhance existing solutions.
Collaborate with cross‐functional project teams to manage smooth execution of data science projects.
Support project managers for integrating data science solutions with software products where applicable.
Support data science study projects through suitable University student programs where applicable.
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.
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 – 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.
Customer Focus Data Modelling Machine Learning Python Anomaly detection Large Language Models SQL Bash/Shell/Powershell AWS S3 DBT Dynamic Modelling Data Analysis Digital Ethics AWS lambda Regression Statistical and Mathematical Analysis Clustering Domain Knowledge Deep Learning Streamlit Robotic Process Automation Agility Classification Data Architecture GitHub AWS Sagemaker Data Engineering Data Preparation DataRobot Data Platform - Snowflake ML Ops Data Visualization Continuous Learning Industry and Domain Knowledge Computer Programming
TechnipFMC respects the rights and dignity of those it works with and promotes adherence to internationally recognized human rights principles for those in its value chain.
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