Job Description
– Data Science / Modelling Lead

Experience:
8+ Years Work Mode: Remote Employment Type: Contract Job Summary We are looking for an experienced Data Science / Modelling Lead with strong expertise in developing, deploying, and managing production-grade data science and machine learning models. The ideal candidate should have hands-on experience delivering multiple ML deployments, particularly in time-series forecasting and anomaly detection, along with expertise in model lifecycle management, drift monitoring, and automated retraining pipelines.

Key Responsibilities
• Lead the design, development, deployment, and maintenance of production-ready data science and machine learning models.
• Build and implement time-series forecasting models for predictive analytics and business planning.
• Develop anomaly-detection models to identify unusual patterns, deviations, and potential risks in data.
• Manage the complete ML lifecycle, from data preparation and feature engineering to model deployment and monitoring.
• Implement model versioning and maintain model repositories for tracking model iterations and performance.
• Establish model drift monitoring mechanisms to identify performance degradation over time.
• Design and implement automated model retraining pipelines to ensure models remain accurate and reliable.
• Evaluate model performance using appropriate statistical and machine learning metrics.
• Collaborate with data engineering, business,



and technology teams to translate business requirements into effective modelling solutions.
• Ensure scalability, reliability, and continuous improvement of deployed models. Mandatory Skills & Experience
• 8+ years of experience in Data Science, Machine Learning, Statistical Modelling, or related domains.
• Hands-on experience with at least 3 production-grade Data Science / ML deployments.
• Proven experience developing and deploying at least one Time-Series Forecasting Model.
• Proven experience developing and deploying at least one Anomaly Detection Model.
• Robust understanding of model versioning and ML lifecycle management.
• Hands-on experience with model drift monitoring and performance tracking.
• Experience building and managing automated model retraining pipelines.
• Strong knowledge of statistical modelling, predictive analytics, and machine learning techniques.
• Experience managing end-to-end production ML deployments. Preferred Qualifications
• Experience with MLOps tools and frameworks.
• Knowledge of Python and relevant ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Experience with ML pipeline orchestration and automation.
• Strong analytical, problem-solving, and technical leadership skills. Candidate Requirements Candidates must demonstrate practical experience with multiple production ML deployments, including time-series forecasting, anomaly detection, model versioning, drift monitoring, and retraining pipelines.

📌 Data Science (India)
🏢 MindBrain
📍 India