Data Scientist

Job Title: Data Scientist

Location: Hybrid onsite in London

Shift: Monday- Friday 40 Hours

Duration: 12 Months (Inside IR35 Umbrella Contract)

Pay: £(Apply online only) per day (depending on experience)

Job Description:

The Data Scientist will be responsible for running analytical experiments in a methodical manner and will regularly evaluate alternate models via theoretical approaches. The role will participate in the team’s engagement with business stakeholders and partners to enhance the existing analytics solutions and developing new solutions to business problems.

The role requires a candidate with vision that will be instrumental in providing inputs to the AI & Data Science, Central Data, and business teams for the design and building of predictive models and algorithms, exploratory data analysis, test design, statistical tests and measures, and business value measurement.

Key accountabilities

• Lead the design, development, and implementation of AI/ML solutions for applications specific to trading, and oil and gas development and operations
  
  
• Design end-to-end ML pipelines that address the full lifecycle of AI/ML solutions from ideation to production deployment, ensuring scalability and business impact.
  
  
• Collaborate with multi-functional teams including geologists, petroleum engineers, and operations staff to translate complex business problems into effective data-driven solutions
  
  
• Drive MLOps best practices, including CI/CD pipelines, model monitoring, and automated retraining workflows.
  
  
• Spearhead research initiatives to solve complex business problems using statistics, ML, and foundation models, while serving as the SME in advanced ML techniques, educating partners on model capabilities and limitations.
  
  
• Identify high-impact AI opportunities, prioritizing initiatives that drive revenue growth and operational efficiency.
  
  
• Partner with Data Engineering to design scalable data pipelines for ML consumption to ensure real-time data integrations and develop model-serving architectures.
  
  
• Apply predictive modeling, LLMs, and deep learning to extract actionable insights from large-scale datasets.
  
  
• Leverage data science tools and techniques in analyzing large datasets that will enable development of custom models and algorithms to uncover insights, trends, and patterns in the data, which will be useful in availing informed courses of action.
  
  
• Responsible for the evaluation of analytics and machine learning technologies for use in the business and communicates findings to key partners through reports and presentations.
  
  
• Partners with other non-technical departments within the business to assist them in understanding how data science can benefit them and improve their effectiveness and performance.
  
  
• Stay ahead of cutting-edge AI advancements (e.g., Generative AI, reinforcement learning) and assess their business viability.
  
  Essential Education & Experience
  
  
• A degree in Statistics, Machine Learning, Mathematics, Computer Science, Economics, or any other related quantitative field.
  
  
• 5+ years of experience in machine learning (supervised, unsupervised, and ensemble methods), natural language processing; deep learning experience is a bonus.
  
  
• 3+ years of experience developing and deploying machine learning models in production environments with demonstrable impact to the business
  
  
• Demonstrated expertise in Python and relevant data science/ML libraries including TensorFlow, PyTorch, scikit-learn, and pandas, and ability to create visualizations and apply persuasive story telling.
  
  
• Hands-on experience with cloud computing platforms (AWS, Azure, GCP) and proficiency with industrial data platforms
  
  
• Proven track record of developing, scaling, and implementing models in customer facing environments.
  
  
• Solid understanding of statistical analysis, experimental design, and data preprocessing techniques for industrial applications
  
  
• Knowledge of DevOps practices and CI/CD pipelines for seamless ML model deployment in production environments
  
  
• Proven ability to design fault-tolerant ML systems with monitoring and automated retraining pipelines, along with model-serving and event-driven architectures
  
  
• Demonstrated ability to conduct rapid Proof of Concepts (POCs) using design thinking methodologies
  
  
• Exposure of modern ML libraries (Hugging Face Transformers, LangChain, LlamaIndex), Spark/PySpark for large-scale data processing
  
  Desireable criteria
  
  
• Ability to lead others to draw conclusions from data and recommend actions.
  
  
• Ability to succeed in a fast-paced environment, deliver high quality performance on multiple, simultaneous strategic, value-added tasks and priorities.
  
  
• Relentless drive, determination, and self-learning ability.
  
  
• Highly organized and attentive to detail