Senior Data Scientist - Deep Learning/GraphQL

About the Role:

We are seeking a talented Senior Data Scientist to join our team and contribute to the development of cutting-edge AI solutions. The ideal candidate will have a strong background in computer vision, GraphQL, and graphical neural networks. You will be responsible for designing, developing, and deploying innovative data-driven solutions to solve complex problems.

Responsibilities:

- Develop and implement machine learning algorithms for computer vision and GraphQL-based applications.

- Collect, clean, and prepare large datasets for analysis.

- Explore and experiment with different machine learning techniques and architectures, including graphical neural networks.

- Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.

- Design and implement GraphQL APIs to expose machine learning models and services.

- Develop and maintain scalable data pipelines and infrastructure.

- Stay up-to-date with the latest advancements in machine learning, AI, and GraphQL.

Requirements:

- 5 to 8 years of strong professional experience working on Machine Learning for PoC as well as Production with International Clients

- Bachelor's degree, Master's degree or PhD in Computer Science, Data Science, or a related field.

- Strong programming skills in Python, with experience in libraries like TensorFlow, PyTorch, OpenCV, and GraphQL.

- Deep understanding of computer vision algorithms and techniques (e.g., object detection, image segmentation, image generation).

- Experience with graphical neural networks (GNNs) and their applications.

- Proficiency in machine learning algorithms and techniques (e.g., regression, classification, clustering).

- Experience with GraphQL API development and GraphQL schema design, Neo4J etc.

- Experience with cloud platforms (e.g., AWS, GCP, Azure) is a plus.

- Excellent problem-solving, analytical, and communication skills.

Preferred Skills:

- Experience with deep learning frameworks like TensorFlow or PyTorch.

- Knowledge of reinforcement learning and generative models.

- Familiarity with cloud-based machine learning platforms and GraphQL-specific tools.