Data Scientist

Title: Senior Data Scientist Location: France, Dubai, or Remote Company Overview Our clients are one of the world’s largest Communications Service Providers (CSPs). Whose software monitors and optimises the networks of eight of the top 10 global telecom groups, ensuring resilience for over 2 billion subscribers worldwide. We’re entering an era of predictive analytics, Generative AI, and agent-based intelligence, allowing us to redefine how networks are managed and automated. At our clients you’ll work with a global, diverse team of innovators passionate about turning data into intelligence, Overview We are seeking a highly skilled Senior Data Scientist to lead the design and implementation of advanced analytical and machine learning models that enable predictive, prescriptive, and automated intelligence. The ideal candidate will have deep expertise in data modelling, forecasting, and anomaly detection, along with the ability to design scalable solutions that transform complex datasets into actionable insights. The role also includes exposure to Generative AI techniques such as LLMs, RAG, and intelligent agents that support enhanced analytics and decision automation. Key Responsibilities 1. Machine Learning & Predictive Analytics Develop and deploy machine learning models for forecasting, anomaly detection, optimization, and root cause analysis. Conduct data exploration and pattern analysis to identify trends, correlations, and behavioural deviations. Apply statistical and algorithmic approaches to improve model performance and interpretability. Validate and monitor models to ensure precision, scalability, and business relevance. 2. Data Preparation & Feature Engineering Work with data engineering teams to establish robust data pipelines and integration frameworks. Develop processes for data cleaning, transformation, correlation, and feature extraction. Ensure data consistency, quality, and traceability across multiple systems and domains. Implement automated workflows to maintain high data integrity and modeling efficiency. 3. Analytical Insight & Decision Enablement Translate complex analytical outcomes into clear, actionable insights that guide decision-making. Collaborate with product and domain experts to identify opportunities for data-driven improvement. Build dashboards and visualizations that communicate model results and performance trends effectively. Quantify the impact of data science initiatives and align with measurable business KPIs. 4. Model Deployment & Lifecycle Management Deploy and maintain ML models using MLOps pipelines with continuous retraining and performance tracking. Implement model monitoring, version control, and drift detection frameworks. Collaborate with DevOps and application teams to integrate analytics components into production environments. Ensure models comply with quality, governance, and reliability standards. 5. Generative AI & Intelligent Systems Apply LLM and RAG-based architectures for knowledge retrieval, contextual reasoning, and data summarization. Develop AI-driven agents that support analytical workflows and decision automation. Experiment with prompt engineering and fine-tuning to enhance model accuracy and adaptability. Combine predictive modelling with generative techniques to enrich data insights and usability. Qualifications Master’s or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field. 7+ years of experience in machine learning, AI, or advanced analytics, with proven impact in model deployment. Strong programming proficiency in Python, R, and SQL, with experience using TensorFlow or PyTorch. Expertise in data modeling, forecasting, classification, clustering, and optimization. Proficiency in data wrangling (pandas, NumPy, PySpark) and visualization tools (Power BI, Tableau, Plotly). Knowledge of MLOps practices, cloud environments (AWS, Azure, GCP), and model performance monitoring. Strong foundation in statistics, probability, and hypothesis testing. Experience with telecom, network assurance, or large-scale telemetry datasets. Familiarity with LLM and RAG implementations, vector databases, and LangChain frameworks. Understanding of AIOps, network analytics, and closed-loop automation. Proven ability to bridge data science and business strategy through measurable outcomes. #J-18808-Ljbffr