Senior Data Scientist

You will be responsible for designing, developing, and deploying AI/ML models to enhance data mapping, anomaly detection, and reconciliation automation within large-scale projects. Your role will involve combining data science expertise with engineering skills to create intelligent systems that optimize Telecom Platform, streamline manual processes, and ensure high-quality results. **Key Responsibilities:** - Design, develop, and deploy ML models for automated data mapping, anomaly detection, reconciliation, fraud detection, and churn prediction. - Perform data profiling, feature engineering, and exploratory analysis to enhance accuracy and performance. - Choose suitable algorithms (supervised, unsupervised, reinforcement learning) based on business requirements. - Construct 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. - Collaborate with cross-functional teams to align AI solutions with Telecom Platform and enterprise needs. - Translate business requirements into clear technical specifications, user stories, and acceptance criteria. - Contribute to platform innovation by adopting the latest AI/ML advancements in anomaly detection and reconciliation automation. - Document AI models, frameworks, and best practices for reusability. - Mentor junior engineers/data scientists to create a collaborative and learning-oriented environment. **Qualifications Required:** - Bachelors or Masters 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. - 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. By joining GlobalLogic, you will be part of a high-trust organization that values integrity and prioritizes a culture of caring. You can expect continuous learning and development opportunities, interesting and meaningful work, balance, and flexibility in your work-life integration. You will be responsible for designing, developing, and deploying AI/ML models to enhance data mapping, anomaly detection, and reconciliation automation within large-scale projects. Your role will involve combining data science expertise with engineering skills to create intelligent systems that optimize Telecom Platform, streamline manual processes, and ensure high-quality results. **Key Responsibilities:** - Design, develop, and deploy ML models for automated data mapping, anomaly detection, reconciliation, fraud detection, and churn prediction. - Perform data profiling, feature engineering, and exploratory analysis to enhance accuracy and performance. - Choose suitable algorithms (supervised, unsupervised, reinforcement learning) based on business requirements. - Construct 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. - Collaborate with cross-functional teams to align AI solutions with Telecom Platform and enterprise needs. - Translate business requirements into clear technical specifications, user stories, and acceptance criteria. - Contribute to platform innovation by adopting the latest AI/ML advancements in anomaly detection and reconciliation automation. - Document AI models, frameworks, and best practices for reusability. - Mentor junior engineers/data scientists to create a collaborative and learning-oriented environment. **Qualifications Required:** - Bachelors or Masters 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. - 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 wit