Sr. Data Scientist

About Us

Join us at Orange Business! We are a network and digital integrator that understands the entire value chain of the digital world, freeing our customers to focus on the strategic initiatives that shape their business. Every day, you will collaborate with a team dedicated to providing consistent, sustainable global solutions, no matter where our customers operate. With over 30,000 employees across Asia, the Americas, Africa, and Europe, we offer a dynamic environment to develop and perfect your skills in a field filled with exciting challenges and opportunities.

Key accountabilities / Key result / decision areas (outcomes)

• Manage and deliver all assigned projects as per agreed time frame, allocated budget, resource and quality criteria.
• Managing project risks, including the development of contingency plans.
• Responsible for identification & all communication with stakeholders.
• Project plan adherence with a view on coordination of multiple projects/activities in the team.
• Timely interaction with stakeholders and update the progress of activity, risks, completion status.
• Regular tracking of project risks and share with relevant stakeholders.
• Work with product, technical and customer support on identifying problems in different areas where machine learning/statistics can help.
• Present findings to both technical and non-technical audiences.
• Lead traversal technical discussion with sales, operation, technology and platform and architecture teams.
• Participate in the entire LLM development lifecycle, from problem definition and data preparation to model training, evaluation, and deployment.
• Identify and own use case coming from different BU and lead it from POC to industrialization.
• Design and implement novel LLM architecture and training techniques, leveraging deep learning frameworks like Tensor Flow and PyTorch.
• Build and share technical architecture with business SPOC.
• Develop and maintain efficient pipelines for data preprocessing, training, and inference.
• Collaborate with data scientists to curate, clean, and prepare high-quality training data for LLMs.
• Develop, contribute and lead technical framework for AI use case environment.
• Evaluate LLM performance using appropriate metrics and identify opportunities for improvement.
• Apply advanced machine learning algorithms, statistical methods and predictive modeling techniques on large and varied data sets that include application logfiles, other online application telemetry, structured and unstructured data sources.
• Deploy LLMs to production environments and monitor their performance for accuracy, fairness, and efficiency.
• Design and develop front-end interfaces for AI-powered applications using modern web technologies such as HTML, CSS, JavaScript, and frameworks like React or Angular.
• Lead research and development initiatives within the team and establish industry‑standard practices for ongoing projects.
• Stay up to date on the latest advancements in LLM research and identify opportunities to incorporate them into your work.
• Act as SPOC from data & AI team to leverage AI capabilities with the business team.
• Build scalable back‑end systems and APIs to support AI model integration and data processing using languages such as Python, Java or Node.js.
• Collaborate with researchers to explore new applications for LLMs in various domains.
• Define solution design proposal for business.
• Manage the infrastructure needed for data scientists to run experiments and deploy models, including setting up GPU/TPU clusters, cloud storage, and containerized environments.
• Document your work clearly and concisely, including research papers, technical reports, and code documentation.

• A minimum of 6 years’ hands‑on applied research experience developing and implementing machine learning models on large‑scale data sets. Experience in developing GenAI products (Advanced RAG, AI Agents, etc.).
• Expertise in machine learning and statistical analysis approaches such as classification, clustering, regression, statistical inference, and collaborative filtering.
• Hands‑on experience conducting analyses on unstructured, structured and semi‑structured data.
• Ability to drive initiatives…