About this role
Purpose of the Job We are seeking a hands-on Data Scientist with strong foundations in statistics and machine learning, experience in cloud-based environments, and exposure to modern AI/GenAI practices. You will build, deploy, and scale production-grade ML and AI solutions in collaboration with business, data engineering, and MLOps teams. Job Description Design, develop, and deploy ML, AI, and GenAI solutions for business problems Own the end-to-end data science lifecycle: problem definition to deployment and monitoring Build and optimize ML/DL, NLP, LLM, and GenAI models (including RAG and prompt engineering) Develop models using Python-based ML frameworks and deploy them at scale Leverage cloud platforms (GCP) for model training, inference, and data processing Implement MLOps best practices (CI/CD, model versioning, monitoring, retraining) Collaborate with data engineers on pipelines, feature stores, and integrations Communicate insights and model outcomes to technical and non-technical stakeholders Mentor junior data scientists and contribute to best practices Job Requirements - Experience and Education Strong proficiency in Python, SQL, and ML libraries (NumPy, Pandas, Scikit-learn) Solid understanding of statistics, probability, and ML algorithms Hands-on experience with TensorFlow, PyTorch, XGBoost, LightGBM Experience with NLP / GenAI / LLMs (embeddings, transformers) Cloud experience on GCP (e.g., Vertex AI) Experience with Docker, APIs (FastAPI/Flask), and MLOps tools (MLflow, Airflow, CI/CD) Bachelor’s or Master’s degree in a relevant field Experience with production ML systems and real-world AI deployments Exposure to responsible AI, explainability, and bias mitigation Prior mentoring or technical leadership experience Leadership Behaviors Building Outstanding Teams Setting a clear direction Simplification Collaborate & break silos Execution & Accountability Growth mindset Innovation Inclusion External focus Skills