Amnet Digital - Senior Data Scientist

Job Title : Senior Data Scientist Location : [Hybrid] Experience : 5+ years About the Role : We are looking for a hands-on Senior Data Scientist to build, deploy, and scale production-grade ML solutions. You will own the end-to-end ML lifecyclefrom data pipelines to deploymentand measure success by real-world business impact. Key Responsibilities : Model Development : - Build and deploy enterprise ML models for forecasting, classification, regression, anomaly detection, and recommendation systems. End-to-End Pipelines : - Own the entire ML pipeline, including data ingestion, feature engineering, model training, validation, and production deployment. Advanced AI/Retrieval : - Apply embeddings, vector search, and representation learning for similarity and retrieval-augmented workflows (RAG). MLOps & Observability : - Drive containerization, CI/CD, versioning, A/B testing, and model monitoring (drift, latency, data quality). Cross-Functional Collaboration : - Translate complex business problems into well-scoped technical solutions alongside engineering and product teams. What Were Looking For : Experience : - 5+ years of experience deploying enterprise-grade ML models in a product setting. Tech Stack : - Strong proficiency in Python and the core ML stack (Scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow). Core Expertise : - Deep knowledge of supervised/unsupervised learning and time-series forecasting. Bonus Skills : - Hands-on experience with vector databases (FAISS, Pinecone, pgvector), LLM/RAG pipelines, and cloud platforms (AWS/GCP/Azure). Job Title : Senior Data Scientist Location : [Hybrid] Experience : 5+ years About the Role : We are looking for a hands-on Senior Data Scientist to build, deploy, and scale production-grade ML solutions. You will own the end-to-end ML lifecyclefrom data pipelines to deploymentand measure success by real-world business impact. Key Responsibilities : Model Development : - Build and deploy enterprise ML models for forecasting, classification, regression, anomaly detection, and recommendation systems. End-to-End Pipelines : - Own the entire ML pipeline, including data ingestion, feature engineering, model training, validation, and production deployment. Advanced AI/Retrieval : - Apply embeddings, vector search, and representation learning for similarity and retrieval-augmented workflows (RAG). MLOps & Observability : - Drive containerization, CI/CD, versioning, A/B testing, and model monitoring (drift, latency, data quality). Cross-Functional Collaboration : - Translate complex business problems into well-scoped technical solutions alongside engineering and product teams. What Were Looking For : Experience : - 5+ years of experience deploying enterprise-grade ML models in a product setting. Tech Stack : - Strong proficiency in Python and the core ML stack (Scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow). Core Expertise : - Deep knowledge of supervised/unsupervised learning and time-series forecasting. Bonus Skills : - Hands-on experience with vector databases (FAISS, Pinecone, pgvector), LLM/RAG pipelines, and cloud platforms (AWS/GCP/Azure).