Predictive Analytics Data Scientist

Predictive Analytics Data Scientist-AI Labs

Responsibilities

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

Qualifications

Educational Qualification:

 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)