Senior Data Scientist

Role Overview: As a highly skilled Data Scientist, your role will involve designing, developing, and deploying AI/ML models that power data mapping, anomaly detection, and reconciliation automation within large-scale projects. You will combine strong data science expertise with practical engineering skills to build intelligent systems that improve Telecom Platform, reduce manual effort, and ensure high-quality outcomes. Key Responsibilities: - Design, develop, and deploy ML models for automated data mapping, anomaly detection, reconciliation, fraud detection, and churn prediction. - Conduct data profiling, feature engineering, and exploratory analysis to improve accuracy and performance. - Select appropriate algorithms (supervised, unsupervised, reinforcement learning) based on business needs. - Build end-to-end ML pipelines for data ingestion, preprocessing, training, validation, and deployment. - Integrate models into Telecom Platform and Automation frameworks for seamless execution in production. - Monitor model performance, implement retraining strategies, and optimize for scalability and reliability. - Work with cross-functional teams to align AI solutions with Telecom Platform and enterprise requirements. - Translate business requirements into clear technical specifications, user stories, and acceptance criteria. - Document AI models, frameworks, and best practices for reusability. - Mentor junior engineers/data scientists, fostering a collaborative and learning-oriented environment. Qualification Required: - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field. - 5 to 8 years of experience in developing and deploying machine learning models in production. - Hands-on experience in Classification, anomaly detection, or reconciliation automation is highly preferred. - Strong knowledge of ML algorithms and techniques (supervised, unsupervised, anomaly detection, NLP, and deep learning). - Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch). - Experience with data pipelines, ETL/ELT, Delta Lake, and data lakehouse architectures. - Cloud-based ML experience (Azure Data Factory, Azure Databricks, AWS Sagemaker, GCP AI/ML). - Skilled in PySpark for large-scale data processing. - Familiarity with containerization (Docker, Kubernetes) for scalable deployment. - Strong grounding in data reconciliation frameworks and automation techniques. - Excellent problem-solving and analytical skills. - Communication and collaboration skills. Additional Company Details: GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world's largest and most forward-thinking companies. Since 2000, GlobalLogic has been at the forefront of the digital revolution - helping create innovative digital products and experiences. The company collaborates with clients in transforming businesses and redefining industries through intelligent products, platforms, and services. Role Overview: As a highly skilled Data Scientist, your role will involve designing, developing, and deploying AI/ML models that power data mapping, anomaly detection, and reconciliation automation within large-scale projects. You will combine strong data science expertise with practical engineering skills to build intelligent systems that improve Telecom Platform, reduce manual effort, and ensure high-quality outcomes. Key Responsibilities: - Design, develop, and deploy ML models for automated data mapping, anomaly detection, reconciliation, fraud detection, and churn prediction. - Conduct data profiling, feature engineering, and exploratory analysis to improve accuracy and performance. - Select appropriate algorithms (supervised, unsupervised, reinforcement learning) based on business needs. - Build end-to-end ML pipelines for data ingestion, preprocessing, training, validation, and deployment. - Integrate models into Telecom Platform and Automation frameworks for seamless execution in production. - Monitor model performance, implement retraining strategies, and optimize for scalability and reliability. - Work with cross-functional teams to align AI solutions with Telecom Platform and enterprise requirements. - Translate business requirements into clear technical specifications, user stories, and acceptance criteria. - Document AI models, frameworks, and best practices for reusability. - Mentor junior engineers/data scientists, fostering a collaborative and learning-oriented environment. Qualification Required: - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field. - 5 to 8 years of experience in developing and deploying machine learning models in production. - Hands-on experience in Classification, anomaly detection, or reconciliation automation is highly preferred. - Strong knowledge of ML algorithms and techniques (supervised, unsupervised, anomaly detection, NLP, and deep learning). - Proficiency in Python and ML libraries (