Predictive Analytics Data Scientist-AI Labs

Purpose/Objective

The Predictive Analytics Data Scientist is responsible for designing, building, and optimizing AI-driven predictive models that enhance business intelligence, risk management, and operational efficiency. This role will focus on end-to-end execution, from data collection and model development to deployment and monitoring, ensuring AI models drive actionable insights for demand forecasting, process automation, and business optimization.

Key Responsibilities of Role

Predictive Analytics Data Scientist-AI Labs Predictive Model Development & Optimization: Develop and train predictive models using time-series forecasting, regression analysis, and anomaly detection to solve business challenges. Optimize predictive accuracy by implementing feature engineering, hyperparameter tuning, and deep learning techniques. Test, refine, and validate AI models through rigorous model evaluation, stress testing, and bias mitigation to ensure reliability. Ensure real-time model scalability by deploying on cloud-based AI infrastructure (AWS, GCP, Azure) using Kubernetes, Docker, and MLOps frameworks. Business Problem Solving & AI Integration: Understand and translate business challenges into AI-driven solutions, ensuring alignment with strategic objectives. Implement AI-powered demand-supply forecasting models to enhance production planning, logistics, and inventory management. Enhance operational efficiency by integrating predictive analytics into decision workflows, process automation, and real-time optimization. Improve cost management and revenue forecasting by leveraging AI models for risk assessment, fraud detection, and business performance monitoring. Data Collection, Feature Engineering & Model Training: Manage structured and unstructured data pipelines, ensuring high-quality inputs for predictive modeling. Conduct feature extraction and engineering to optimize data representation and improve model performance. Automate model training pipelines to enhance scalability, version control, and real-time learning capabilities. Model Deployment, Monitoring & Performance Evaluation Deploy AI models into production, ensuring seamless integration into enterprise applications and business workflows. Monitor model performance in real-world scenarios, identifying and addressing model drift, bias, and performance degradation. Continuously improve model efficiency through iterative learning, retraining cycles, and adaptive optimization techniques. Collaboration with AI, Engineering & Business Teams: Work closely with model deployment teams to ensure AI solutions are effectively operationalized. Coordinate with front-end developers to integrate predictive insights into AI-powered dashboards and user applications. Partner with business teams to align AI models with key performance indicators (KPIs) and measurable business impact. AI Governance, Compliance & Risk Management: Ensure AI compliance with industry regulations, data privacy laws (GDPR, AI Act), and company-wide governance frameworks. Implement explainability frameworks to improve transparency and interpretability of predictive models. Conduct risk assessments to identify and mitigate model bias, ethical concerns, and unintended consequences. Key Stakeholders - Internal AI & Data Science Teams Business Leaders & Strategy Teams IT & Cloud Engineering Teams Finance & Risk Management Teams Key Stakeholders - External Technology Partners & AI Vendors Research Institutions & Academia Regulatory Bodies

Technical Competencies

AI Governance, Compliance & Ethical AI-SDIL,AI Model Development & Optimization-SDIL,AI Product & Solution Development-SDIL,AI Research, Experimentation & Emerging Technologies-SDIL,AI Strategy & Enterprise Transformation-SDIL,Cross-Functional Collaboration & AI Integration-SDIL,Predictive Analytics & Business Intelligence-SDIL

Qualifications and Experience

Educational Qualification: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or related fields. Certification: Certified Machine Learning Engineer (Google AWS Microsoft AI Certification) Professional Certification in Predictive Analytics (Coursera IBM DataCamp) MLOps & Cloud AI Certification (AWS GCP Azure) Work Experience (Range of years): 5-7 years of experience in developing and deploying predictive analytics models, machine learning algorithms, and AI-driven solutions.