Lead Data Scientist

Job Title - Lead Data Scientist - Level 9 - ACS Song Management Level: Level 9 Lead/Specialist Location: Kochi Must have skills: Data Science and Machine Learning Good to have skills: Experience: 5-8 years of experience is required Educational Qualification: Graduation (Accurate educational details should capture) Job Summary We are seeking a Senior Data Scientist specializing in production-grade Machine Learning model development and deployment with 5+ years of experience in data science, machine learning, statistical modeling, and applied AI solutions. The role will focus on designing, developing, validating, and operationalizing machine learning models that solve real business problems and are deployed into production environments rather than limited proof-of-concept implementations. The ideal candidate should have hands-on experience with at least one major cloud platform such as Azure, GCP, or AWS, along with strong knowledge of Python, ML frameworks, model evaluation, feature engineering, MLOps, and production model monitoring. This position requires close collaboration with data engineers, ML engineers, architects, product teams, and business stakeholders to deliver scalable, reliable, explainable, and business-impact-driven ML solutions. Roles And Responsibilities Develop, validate, and deploy machine learning models that are production-ready and aligned with business objectives. Translate business problems into analytical, statistical, and machine learning solutions with measurable outcomes. Apply strong MLOps practices to ensure models are versioned, monitored, retrained, governed, and maintained after deployment. Collaborate with engineering and business teams to move ML solutions from experimentation to scalable production implementation. Identify business problems and design appropriate machine learning, statistical, or AI-based approaches to solve them. Build, train, validate, and optimize machine learning models using production-ready data science and engineering practices. Perform feature engineering, model selection, hyperparameter tuning, model evaluation, explainability analysis, and performance benchmarking. Deploy ML models into production environments in collaboration with ML engineers, data engineers, DevOps teams, and cloud platform teams. Monitor production models for performance, data drift, model drift, bias, latency, reliability, and business impact. Work experience with cloud platforms such asAzure, GCP, or AWSfor model development, training, deployment, experimentation, and monitoring. Communicate model outcomes, limitations, risks, assumptions, and business value clearly to technical and non-technical stakeholders. Professional and Technical Skills: 5+ years of professional experience in data science, machine learning, applied AI, statistical modeling, or related roles. Hands-on experience developing and deploying ML models into production environments, not only proof-of-concept or experimental setups. Experience working with cloud-based ML platforms or services on Azure, GCP, or AWS. Proven experience applying machine learning to real-world business problems with measurable outcomes. Industry specialization in Retail, Ecommerce and Automotive is a plus. Strong programming skills in Python, (or R) for data science, machine learning, experimentation, and production-oriented development. Strong understanding of supervised learning, unsupervised learning, classification, regression, clustering, forecasting, anomaly detection, and model evaluation techniques. Hands-on experience with ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, MLflow, or similar tools. Good knowledge of SQL and experience working with structured, semi-structured, and large-scale datasets. Experience with feature engineering, data preprocessing, model explainability, bias evaluation, and performance optimization. Familiarity with cloud ML services such as Azure Machine Learning, GCP Vertex AI, AWS SageMaker, or equivalent platforms. Experience with Git, experiment tracking, CI/CD concepts, model registries, containerization, logging, monitoring, and production release workflows. Strong understanding of the end-to-end ML lifecycle from problem framing and experimentation to deployment and monitoring. Experience with model versioning, experiment tracking, model registry, reproducible pipelines, and automated model evaluation. Ability to design and support batch inference, real-time inference, scheduled retraining, and model performance monitoring. Understanding of model governance, responsible AI, data privacy, explainability, auditability, and secure handling of model artifacts. Ability to troubleshoot production issues related to model degradation, data quality, drift, latency, scalability, and prediction accuracy. Additional Information Behavioral and Collaboration Skills Strong analytical, troubleshooting, and problem-solving skills. Ability J