Data Scientist - Speech Recognition

Description : Speech Data Scientist

Experience Level : 3-6 years

- Design and implement end-to-end speech analytics pipelines for production environments

- Develop ASR engines using state-of-the-art frameworks (Wav2vec, Whisper, Deep Speech) with PyTorch or TensorFlow

- Build and optimize speaker diarization, language identification (LID), and text post-processing systems

- Focus on multilingual audio processing capabilities

- Lead data selection strategies for domain adaptation and model optimization

Model Development & Enhancement :

- Develop and analyze objective measures for speech quality evaluation and enhancement

- Implement speaker-conditioned personalization techniques for improved ASR accuracy in noisy environments

- Optimize on-device ASR models with emphasis in multilanguage scenarios

- Guide teams on best practices for model accuracy improvement and performance optimization

Research & Innovation :

- Conduct research on advanced speech processing techniques including neural speech enhancement

- Develop novel approaches for complex audio scenarios and multi-speaker environments

- Contribute to patent applications and research publications in speech technology

- Stay current with latest developments in transformer models, attention mechanisms, and foundation models

Technical Integration & Deployment :

- Design integration architectures for speech-to-text services and supporting technologies

- Implement MLOps processes and CI/CD pipelines for speech models

- Deploy and scale speech solutions on cloud platforms (AWS, GCP)

- Develop production-ready applications using Python, C++, and Java

Educational Background :

- Ph.D/M.Tech/M.S in relevant field (Computer Science / Signal Processing) preferred

- B.Tech/B.E in ECE, CSE, or related technical field

Technical Expertise :

Core Speech Processing :

- 3-6 years of hands-on experience in speech recognition and processing

- Deep understanding of classical methodologies: HMMs, GMMs, ANNs, Language modeling

- Expertise in modern deep learning techniques: CNNs, RNNs, LSTMs, CTC, Attention mechanisms

- Strong background in digital signal processing and audio analysis

Machine Learning & Deep Learning :

- Proficiency with PyTorch and TensorFlow frameworks

- Experience with transformer models (BERT, Wav2vec 2.0, Wisper)

- Knowledge of end-to-end ASR implementation and optimization

- Understanding of foundation models and transfer learning approaches

Programming & Tools :

- Strong Python programming skills with ML/DL libraries (numpy, pandas, scikit-learn)

- Experience with C++ and Java for production implementations

- Proficiency in bash scripting and automation

- Familiarity with version control (Git) and collaborative development

Cloud & Deployment :

- Hands-on experience with cloud platforms (AWS, GCP)

- Knowledge of containerization (Docker, Kubernetes)

- Experience with MLOps tools and CI/CD pipelines

- Understanding of model serving and scalability considerations