Lead Data Scientist

2 months ago

Gurugram, India Orange Business Services Full time

about the role

Design, develop, and deploy machine learning models that meet business needs and deliver measurable impact. Manage the end-to-end lifecycle of AI/ML models, including data gathering, feature engineering, model training, testing, and deployment. Collaborate with data engineers, product managers, and business stakeholders to understand project requirements and deliver impactful solutions. Guide the development and optimization of large-scale data pipelines to support model training and deployment. Develop, Implement, and enhance models in areas such as predictive analytics, GenAI, natural language processing (NLP), deep learning, and recommendation systems. Ensure the scalability, efficiency, and accuracy of machine learning models in production environments. Mentor and guide junior data scientists and ML engineers, fostering a culture of learning and innovation. Stay up-to-date with the latest industry trends, technologies, and research in machine learning, AI, GenaI and data science, and apply relevant insights to projects. Communicate technical concepts and results effectively to business stakeholders and senior leadership. Work closely with DevOps and engineering teams to manage the lifecycle of ML models, including versioning, monitoring, and maintenance. Contribute to the strategic direction of the AI/ML/GenaI practice within the organization.

about you

6+ years of experience in data science / machine learning, with at least 2 years as a Senior Data Scientist/ML Engineer with the following skills and tools/technologies:

Bachelor’s/PG degree in Engineering, Computer Science, Data Science, Statistics or a related field with a focus on analytics skills. Proven experience leading end-to-end machine learning projects, from conceptualization to deployment. Strong knowledge of machine learning algorithms, model evaluation metrics, and best practices for model deployment. Proficiency in Python used in data science and ML. Familiarity with big data technologies and cloud services. Working knowledge of a variety of machine learning techniques and concepts (regression, time series, classification, Ensemble modeling, Gradient, Boosting, stacking, clustering, decision trees, Neural Networks, image/text processing/NLP, AI, XAI, Transformers/LLM/GenAI and RAG etc.) Solid understanding of statistical analysis, data mining, and data visualization techniques. Good to have experience with MLOps practices to ensure the smooth operation of models in production. Hands-on experience with machine learning libraries (, TensorFlow, PyTorch, Scikit-learn, pretrained ML models, Transformers, RAG ), and advanced SQL. Ability to tackle complex problems, break them down into actionable steps, and deliver practical solutions. Excellent verbal and written communication skills, with the ability to translate complex data insights into clear and actionable recommendations. Understanding of how to align machine learning and AI solutions with business goals. Ability to work cross-functionally with teams across engineering, product, and business domains. Experience with cloud platforms, including Google Cloud Platform (GCP) , Azure for machine learning / GenAI.

additional information

Experience with deep learning, NLP, Transfer learning, GenAI, LLM and time-series modeling. Experience with CI/CD pipelines for model deployment. Familiarity with graph databases, RAG, Vector DB or other non-traditional data storage solutions. Experience with large-scale data processing frameworks (, Hadoop, Spark) and cloud platforms (AWS, Azure, GCP (Preferred)). Nice to have prior experience in Telecom domain.

department

Chief Technology Info Office

Orange Business manages and integrates the complexity of international communications, freeing our customers to focus on the strategic initiatives that drive their business. Our extensive experience and knowledge in global communication solutions, together with our understanding of multinational business and local support in 166 countries and territories, ensure that our customers receive a consistent, global solution wherever they do business

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