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Lavori da Laurea Matematica
Senior data scientist (roma)
Dacomat srl
Roma
In loco
EUR 75.000 - 100.000
Tempo pieno
30+ giorni fa

Descrizione del lavoro

Un'importante azienda multinazionale di telecomunicazioni è alla ricerca di un Senior Data Scientist per guidare iniziative di modellazione predittiva e sviluppo di AI agentico. Si richiedono almeno 10 anni di esperienza nel settore IT e 5 anni in AI/ML. Il candidato ideale possiede competenze in Machine Learning, programmazione Python e analisi dei dati. Si offre un ambiente stimolante e l'opportunità di influenzare operazioni finanziarie strategiche.

Competenze

  • Minimo 10 anni di esperienza nel settore IT.
  • Almeno 5 anni di esperienza in progetti di AI e Machine Learning.
  • Conoscenza delle strutture finanziarie telecom.

Mansioni

  • Progettare e implementare modelli analitici per la previsione dei ricavi.
  • Sviluppare sistemi di AI agentici per decisioni autonome.
  • Collaborare con i team finance e prodotto per soluzioni basate su dati.

Conoscenze

Machine Learning
Statistical Modeling
Data Analysis
Python Programming

Formazione

Master’s or PhD in Data Science, Computer Science, Engineering, Statistics, Physics or Mathematics

Strumenti

PySpark
SQL
Cloud Platforms (Azure)
MLOps
Descrizione del lavoro
Overview

A leading multinational group in the telecommunications and technology sector, is seeking a highly skilled and forward-thinking Senior Data Scientist to lead initiatives in predictive modeling for telecommunications financials and Agentic AI development. This role combines deep statistical modeling expertise with cutting-edge AI capabilities to drive strategic insights, automation, and intelligent decision-making across financial operations and customer-facing systems.

The ideal candidate will have extensive hands-on experience in modeling, implementing, and managing Artificial Intelligence and Machine Learning models and Agentic AI solutions in the telecommunications or finance industries. The candidate should have over 10 years of professional experience in the Information Technology sector and at least 5 years of proven experience in Artificial Intelligence and Machine Learning projects or analytics and data science teams at large corporations. Experience in Finance and Performance Management Analytics for Telecommunication Industry is highly preferable.

The candidate should demonstrate a solid foundation in mathematics, statistics, and machine learning algorithms - including deep learning and neural networks-, visual mining, exploratory data analysis (EDA), data wrangling, and preprocessing.

The candidate should also demonstrate knowledge of core computer science principles, including algorithms and data structures, computer architecture, operating systems, programming languages, compilers and interpreters, and the theory of computation.

Key Responsibilities
  • Design and implement analytical and predictive models for revenue/cost forecasting and management, churn prediction, cost optimization, and financial risk analysis.
  • Develop and deploy Agentic AI systems capable of autonomous decision making and multiagent collaboration.
  • Collaborate with finance, operations, and product teams to translate business objectives into data driven solutions.
  • Ensure model interpretability, fairness, and regulatory compliance.
  • Lead experimentation and validation cycles using real world telco datasets.
  • Contribute to the strategic roadmap for AI driven financial transformation.
Education
  • Master’s or PhD in Data Science, Computer Science, Engineering, Statistics, Physics or Mathematics.
Required Technical Skills
  • Proficiency in advanced techniques in time series analysis: ARIMA, Exponential Smoothing, Box Jenkins, etc.
  • Proficiency in AI & Machine Learning Models: Regression Analysis, Scenario Analysis, Clustering & Segmentation, Random Forests & Decision Trees, Neural Networks (RNN, LSTM), Support Vector Machines (SVM), etc.
  • Knowledge of econometrics and optimization models: Linear and Nonlinear Programming optimization, Stochastic optimization, Metaheuristics optimization, Vector Autoregression (VAR), Mixed-Integer Optimization, etc.
  • Strong Experience in Model Evaluation and Validation: Cross-Validation, Performance Metrics, Overfitting & Regularization, etc.
  • Ability to architect and train multimodal AI agents: implement agentic solutions using LLMs, simulation environments, and planning frameworks. Handson experience with Agentic AI architecture, including planning, memory, and tool use.
  • Strong programming proficiency in Python (including PySpark), SQL, and ML frameworks. Hands-on experience with LLM orchestration frameworks (e.g., LangChain, Semantic Kernel, AutoGen, MS Copilot, etc.).
  • Understanding of telco financial structures (ARPU, OPEX/CAPEX, customer lifecycle metrics).
  • Knowledge of cloud platforms (Azure preferred)
  • Knowledge and experience with MLOps, CI/CD and distributed version control systems (preferred Git).
Soft Skills & Leadership Competencies
  • Strategic Thinking – Ability to align data science initiatives with business goals and long-term strategy.
  • Communication & Storytelling – Skilled in translating complex models into actionable insights for non-technical stakeholders.
  • Collaboration – Proven ability to work cross-functionally with finance, engineering, and executive teams.
  • Problem Solving – Strong analytical mindset with a proactive approach to identifying and solving business challenges.
  • Adaptability – Comfortable working in fast-paced environments and adapting to evolving technologies.
  • Leadership & Mentorship – Experience guiding junior team members and fostering a collaborative team culture.
  • Ethical Judgment – Awareness of responsible AI practices, data privacy, and compliance.
Application

Se interessato, può allegare un proprio CV all’indirizzo email: m.dangelo@dacomat.com esplicitando:

  • disponibilità temporistiche (eventuale preavviso)
  • aspettativa economica (RAL annua, netto mensile desiderato / daily rate)
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* Il benchmark retributivo si basa sugli obiettivi retributivi dei leader del mercato nei rispettivi settori. È pensato per orientare gli utenti Premium nella valutazione delle posizioni aperte e aiutarli a negoziare la propria retribuzione. Tale benchmark non è fornito direttamente dall'azienda, quindi la retribuzione effettiva potrà risultare anche notevolmente superiore o inferiore.

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