Data Science Engineer

Role & responsibilities

Machine Learning (ML)

ML models power predictive analytics, enabling applications such as demand forecasting, fraud detection, and anomaly detection.

Deep Learning (DL)

DL - Knowledge on advanced neural networks to drive breakthroughs in speech recognition, recommendation engines, and fraud detection.

AI Use Cases if any of below:

AI Bots for Efficiency

Chatbots for Supplier & Customer Support

Automated Report Generation

Predictive Maintenance

Process Optimization

Hyperscaler:

Real-time facial recognition (e.g., Face ID, surveillance)

Autonomous driving (object detection, segmentation)

Retail analytics (shelf monitoring, customer behavior)

Industrial inspection (defect detection at scale)

Healthcare imaging (tumor detection, segmentation)

Hyperscaler Advanced if any:

Elastic Scaling

Pre-trained Models & APIs

Global Reach & Latency Optimization

Security & Compliance(ex: Built-in support for data privacy, encryption, and regulatory compliance (SOC2, HIPAA))

Developer Productivity(ex:Tools like SageMaker, Vertex AI, and Azure ML Studio simplify model development, deployment, and monitoring)