Data Scientist

Luxoft; United Kingdom; Remote; GBP 50,000 - 75,000; Full time

Job summary

A leading technology and consulting firm in the UK is seeking a skilled Data Scientist to develop machine learning models and derive data-driven insights for Digital Oilfield systems. The role involves analyzing operational data, collaborating with engineers to define KPIs, and deploying models in production environments. Ideal candidates should have a strong statistical background, proficiency in Python, and experience with large datasets. Knowledge of the oil & gas industry is a plus, but not mandatory.

Qualifications

• Strong statistical background with real-world datasets.
• Proficient in relevant programming languages and techniques.

Responsibilities

• Analyze operational datasets to extract insights.
• Build and evaluate ML models for various applications.
• Collaborate with teams to deploy models and monitor performance.

Skills

Statistical background and data science expertise

Proficient in Python (NumPy, pandas, scikit-learn, XGBoost)

SQL fluency and data wrangling skills

Experience with large, messy, or multivariate time series data

Ability to communicate complex model behavior

Comfortable working in cross-functional teams

Tools

Azure ML

Databricks

Job description

Overview

We are looking for a hands-on Data Scientist to lead the development of machine learning models and data-driven insights for Digital Oilfield (DOF) systems. This role requires a strong foundation in data science and statistical modeling, with the ability to transform noisy, real-world oilfield data into actionable intelligence for engineering teams. Candidate will work alongside production engineers, software developers, and ML engineers to design, validate, and operationalize predictive and prescriptive analytics that support key decisions in subsurface and surface operations. Oil & gas experience is a strong asset but not mandatory; the ideal candidate brings analytical rigor and problem-solving expertise to solve complex field challenges.

Responsibilities

• Analyze diverse operational datasets (time series, tabular, sensor logs, etc.) to extract insights and guide model development.
• Build and evaluate ML models for forecasting, anomaly detection, pattern recognition, and classification.
• Work with engineers to define KPIs and convert domain-specific questions into quantifiable modeling tasks.
• Design meaningful visualizations and dashboards to communicate model outputs clearly.
• Collaborate with ML Ops and software teams to deploy models into production environments and monitor performance.
• Business trip to Kuwait.

Skills

Must have

• Strong statistical background and data science expertise with real-world datasets.
• Proficient in Python (NumPy, pandas, scikit-learn, XGBoost, etc.); SQL fluency and data wrangling skills.
• Experience working with large, messy, or multivariate time series data.
• Ability to communicate complex model behavior to engineers and stakeholders.
• Comfortable working in cross-functional teams with domain and technical experts.

Nice to have

• Oilfield data exposure (e.g., well data, reservoir simulations, production logs) or interest in industrial applications.
• Familiarity with DOF systems or production optimization frameworks.
• Exposure to LLMs, NLP techniques, or agent-based AI for enhancing technical workflows.
• Cloud familiarity (Azure preferred); knowledge of ML platforms (e.g., Azure ML, Databricks).
• Certifications: Azure Data Scientist Associate or Microsoft AI Fundamentals certification is a plus.
• AWS cloud knowledge is welcome but not required.