Data Scientist Voice Bot

Responsibilities

• Develop and deploy machine learning, NLP, and speech-based models for BFSI datasets.
• Build end-to-end Voicebots covering ASR, NLU, dialogue management, and TTS pipelines.
• Work with speech recognition engines (Whisper, Google ASR, Azure Cognitive Speech, Amazon Transcribe) and text-to-speech systems(Google WaveNet, Amazon Polly, Azure TTS).
• Create and optimise Chatbots using Rasa, Dialogflow, LangChain, or LLM-based frameworks.
• Implement entity extraction, intent classification, embeddings, and transformer-based models.
• Perform data preprocessing, feature engineering, and model optimisation for large-scale datasets.
• Integrate AI models into production environments with engineering teams using REST APIs, Docker, and cloud services.
• Analyse model performance, improve accuracy, and ensure low-latency speech/voice response systems.
• Collaborate on the design of conversational flows, voice UX, and call automation logic.
  

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, or related fields.
• Strong proficiency in Python, Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch.
• Hands-on experience with ASR / TTS technologies: OpenAI Whisper, Google Speech-to-Text, Azure Cognitive Speech, Amazon Transcribe, TTS engines like WaveNet, Amazon Polly, Azure Neural TTS
• Expertise in NLP, NLU, and Conversational AI frameworks such as Rasa, Dialogflow, LangChain, and uggingFace Transformers.
• Experience in building Voicebots, Chatbots, and conversational flows.
• Familiarity with BFSI datasets and domain-specific modelling challenges.
• Cloud experience with AWS / Azure / GCP for model deployment (Lambda, EC2 S3 Cloud Functions).
• Strong problem-solving skills and ability to communicate technical insights clearly.
• Knowledge of Twilio Voice, SIP/VoIP systems, or call automation platforms.
• Experience with real-time streaming pipelines for speech processing.
• Exposure to vector databases (FAISS, Pinecone) for RAG-based chatbots.
• Experience using Docker, CI/CD pipelines, and microservices architecture.
• Proven experience in building and deploying Voicebots (ASR + NLU + TTS end-to-end).
• Practical experience creating Chatbots for customer support or automation.
• Experience in AI implementation within financial services.
• Cloud-based ML deployment exposure (AWS, Azure, GCP).