Head - Artificial Intelligence - Sports Analytics

Responsibilities:

Visionary Leadership:

- Craft and continually refine Stupa Analytics' AI vision.

- Synchronize this vision with the company's macro objectives and the evolving landscape of sports analytics.

AI Strategy Formulation and Execution:

Synthesize with senior leadership to:

a) Outline and refine AI strategic objectives and key results.

b) Align AI initiatives with potential impact and feasibility.

c) Ensure the timely and effective execution of the AI roadmap.

Technical Implementation:

- Real-time Sports Analytics: Develop and optimize models for features like auto-scoring, autocommentary, player trajectory, speed, and ball tracking.

- Computer Vision Excellence: Implement advanced object detection, pose estimation, and segmentation to provide in-depth match analysis.

- Optimization: Ensure models are tailored for real-time performance, especially during live streaming, balancing accuracy and computational efficiency.

- Edge Computing: Drive the deployment of AI models on IoT devices and edge platforms for instantaneous inferencing.

Research & Development:

- Lead research efforts to stay ahead of AI, machine learning, and computer vision advancements, integrating cutting-edge techniques into Stupa's offerings. Interdisciplinary Collaboration: Be the technical bridge between data scientists, developers, designers, and other stakeholders for seamless AI solution integration.

- MLOps and Deployment: Design, develop, and oversee robust MLOps pipelines, ensuring efficient deployment on cloud, on-prem, and edge devices.

Requirements:

- Educational Background: Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field from a tier 1 college.

Technical Expertise:

- Deep Dive into Computer Vision: Proven experience in real-time inferencing, object detection, tracking, segmentation, and pose estimation.

- Sports Analytics Nuances: Experience in features like auto-scoring, ball trajectory, speed analysis, and auto-commentary.

- Cloud and On-prem Solutions: Proficiency in deploying solutions on platforms such as GCP, AWS, Azure, and on-premises hardware.

- Experience: 10+ years of intensive hands-on experience in AI, deep learning, and computer vision.

- Programming Skills: Mastery over Python and strong experience with frameworks like TensorFlow, PyTorch, and OpenCV

- Communication Skills: Exceptional ability to articulate complex technical concepts clearly and concisely.

- Innovative Thinking: A knack for identifying novel solutions and a data-first approach to problem-solving.

Preferred:

- Previous engagement with the sports analytics industry.

- Experience in integrating AI models with live streaming technologies.