Data Scientist

Job DescriptionJOB DESCRIPTIONTitle: 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 andoptimises 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 managedand our clients you’ll work with a global, diverse team of innovators passionate about turningdata into intelligence, Overview

We are seeking a highly skilled Senior Data Scientist to lead the design andimplementation of advanced analytical and machine learning models that enablepredictive, prescriptive, and automated intelligence. The ideal candidate will have deepexpertise in data modelling, forecasting, and anomaly detection, along with the ability todesign scalable solutions that transform complex datasets into actionable insights. Therole also includes exposure to Generative AI techniques such as LLMs, RAG, andintelligent agents that support enhanced analytics and decision Responsibilities1. 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, andbehavioural deviations. Apply statistical and algorithmic approaches to improve model performance andinterpretability. Validate and monitor models to ensure precision, scalability, and businessrelevance.2. Data Preparation & Feature Engineering Work with data engineering teams to establish robust data pipelines andintegration frameworks. Develop processes for data cleaning, transformation, correlation, and featureextraction. Ensure data consistency, quality, and traceability across multiple systems anddomains. Implement automated workflows to maintain high data integrity and modelingefficiency.3. Analytical Insight & Decision Enablement Translate complex analytical outcomes into clear, actionable insights that guidedecision-making. Collaborate with product and domain experts to identify opportunities for data-driven improvement. Build dashboards and visualizations that communicate model results andperformance 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 continuousretraining and performance tracking. Implement model monitoring, version control, and drift detection frameworks. Collaborate with Dev

Ops and application teams to integrate analyticscomponents 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, contextualreasoning, and data summarization. Develop AI-driven agents that support analytical workflows and decisionautomation. Experiment with prompt engineering and fine-tuning to enhance model accuracyand adaptability. Combine predictive modelling with generative techniques to enrich data insightsand usability. Qualifications Master’s or Ph.

D. in Data Science, Computer Science, Statistics, Mathematics, orrelated quantitative field.

• 7+ years of experience in machine learning, AI, or advanced analytics, with provenimpact in model deployment. Strong programming proficiency in Python, R, and SQL, with experience using

Tensor

Flow, or Py

Torch.

• Expertise in data modeling, forecasting, classification, clustering, andoptimization. Proficiency in data wrangling (pandas, Num

Py, Py

Spark) and visualization tools(Power BI, Tableau, Plotly).

• Knowledge of MLOps practices, cloud environments (AWS, Azure, GCP), andmodel 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 Lang

Chainframeworks.

• Understanding of AIOps, network analytics, and closed-loop automation. Proven ability to bridge data science and business strategy through measurableoutcomes.