Data Science Manager

Key Responsibilities

1.
Applied Data Science and Machine Learning: Translate business and operational problems into data science solutions. Build statistical, predictive, and machine learning models for forecasting, anomaly detection, and optimization.

2.
Advanced Machine Learning and Deep Learning: Design, train, evaluate, and deploy advanced machine learning and deep learning models while balancing accuracy, performance, and scalability.

3.
Computer Vision and CNN Systems: Build camera-based systems using convolutional neural networks. Support detection, classification, tracking, and estimation use cases. Design data labeling, augmentation, and validation pipelines.

4.
IoT, Sensor Data, and Edge Intelligence: Work with sensor and time series data. Combine signal processing with machine learning. Define edge versus cloud inference strategies and deploy models into constrained environments.

5.
Advanced AI and LLM Applications: Apply large language models in enterprise systems. Implement fine tuning, retrieval augmented generation, and cutting edge AI architectures in global production grade environment.

6.
Architecture and MLOps: Design data pipelines, feature engineering workflows, and model lifecycle processes including deployment, monitoring, and retraining.

7.
Leadership and

Collaboration:

Mentor lead researchers, data scientists and ML engineers. Provide technical direction, lead project management and data science strategy while working closely with engineering, product, and platform teams.

8.
SME and Growth Support: Build IP, POVs and While Papers as a thought leader and research specialist. Collaborate with Sales teams to design customer aligned innovative solutions.

1. Master’s degree in data science, Machine Learning, Artificial Intelligence, Computer Vision, Robotics, Engineering, Mathematics, Physics, or related field. Strong foundation in statistics, machine learning, and systems.

2. Proven industry experience delivering machine learning and AI systems into production environments.

3. Tableau or R experience.

4. Python, Pytorch, Tensor Machine Learning Platforms,

5. Azure IoT Edge for IoT Platform

1. PhD with applied or industry-aligned research in AI, ML, computer vision, signal processing, robotics, or IoT is a strong plus.

2. Experience in SaaS, IoT, industrial systems, robotics, or applied AI domains.

3. Experience with research labs

Deep expertise in data science, machine learning, and applied AI. Strong grounding in computer vision, CNNs, and IoT analytics. Ability to translate research concepts into production systems. Clear communication and strong systems thinking.