Data Scientist Lead/AD

Job Summary

As a leader in the EDO, Collection Platforms & AI Cognitive Engineering team, you will own the vision and delivery of enterprise-scale Data Science and GenAI solutions. You will define the data science strategy , champion best practices , mentor a world-class team of data scientists and ML engineers , and drive production-ready AI/ML products from ideation through deployment. This role involves working in a global team that values thoughtful risk-taking and self-initiative.

Key Responsibilities

• Shape and execute the Data Science roadmap for ML, GenAI, and ASR, including tooling, methodologies, and best practices.
  
• Architect, develop, and deploy large-scale ML and GenAI pipelines, including Voice Activity Detection (VAD), Speaker Diarization, and ASR models.
  
• Own the full data science lifecycle, from opportunity identification and requirement gathering to modeling, deployment, monitoring, and optimization.
  
• Drive MLOps practices, including CI/CD for models, feature stores, monitoring, and automated rollback strategies.
  
• Mentor and develop junior and mid-level data scientists, fostering a culture of continuous learning and collaboration.
  
• Partner with cross-functional stakeholders to align on project goals, timelines, and SLAs.
  

Core Requirements

• 8+ years of hands-on experience in Data Science/AI, with at least 3 years in a senior or leadership role.
  
• Proven expertise in developing and deploying ASR systems, including training models (e.g., Whisper), designing VAD pipelines, and implementing Speaker Diarization (e.g., Pyannote).
  
• Deep knowledge of large language models (e.g., OpenAI, Anthropic, Llama), prompt engineering, fine-tuning, and embedding-based retrieval.
  
• Expert proficiency in Python and its core libraries, including NumPy, Pandas, SpaCy, scikit-learn, PyTorch/TF 2, and Hugging Face Transformers.
  
• Strong understanding of ML & deep learning architectures like NLP transformers and GNNs.
  
• Experience with orchestration and deployment tools such as Docker, Kubernetes, Airflow, and cloud services (AWS/GCP/Azure).
  

Good to Have

• A Master's or Ph.D. in a related technical field (Computer Science, Statistics, etc.).
  
• Prior experience in the Economics/Financial industry.
  
• Public contributions on platforms like GitHub, Kaggle, or technical blog