Data Scientist

a Scientist

Full time

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Cape Town | Paris | Dubai | Europe Remote

5 - 20 Years of Experience

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JOB DESCRIPTION

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.