Data Scientist

Role & responsibilities

We are seeking a talented and motivated Data Scientist specializing in Large Language Models (LLMs) to join our team. You will play a pivotal role in designing, fine-tuning, and implementing advanced conversational AI models to enhance our customer service solutions. This position focuses on tailoring LLMs to effectively handle a wide range of customer interactions, including bookings, payments, and exception scenarios such as managing angry customers, responding to sarcastic remarks, addressing out-of-context questions, and ensuring responses are contextually accurate and empathetic.

Key Responsibilities

Model Fine-Tuning and Optimization

Fine-tune large language models (e.g., OpenAI, GPT, or other state-of-the-art frameworks) to align with business-specific customer service needs.

Adjust model parameters to improve performance on use-case-specific datasets.

Development and Training

Design and implement training workflows using retrieval-based techniques, prompt engineering, and fine-tuning as necessary.

Build and maintain datasets, including creating, annotating, and cleaning customer interaction data for training purposes.

Response Contextualization

Develop models that can handle complex customer queries with contextual relevance, ensuring responses are coherent, empathetic, and aligned with business objectives.

Incorporate strategies to handle sarcasm, emotional outbursts, and out-of-context queries gracefully.

Integration and Deployment

Collaborate with engineering teams to deploy and monitor the performance of LLMs in production systems.

Optimize the models for scalability, latency, and cost-effectiveness in a high-traffic environment.

Research and Innovation

Stay updated on advancements in LLMs, conversational AI, and customer service automation.

Experiment with state-of-the-art techniques like reinforcement learning with human feedback (RLHF) to improve model performance.

Evaluation and Feedback Loop

Define and measure key performance indicators (KPIs) such as accuracy, relevance, and customer satisfaction.

Incorporate feedback loops to iteratively refine models based on user interactions and edge cases.

Preferred candidate profile

Qualifications and Skills

Education: Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field (PhD is a plus).

Experience:

Proven experience working with large language models (e.g., OpenAI GPT, LLaMA, etc.) and fine-tuning techniques.

Hands-on experience with conversational AI systems in customer service or similar domains.

Technical Expertise:

Strong programming skills in Python, with proficiency in machine learning libraries such as TensorFlow, PyTorch, or Hugging Face Transformers.

Expertise in prompt engineering, retrieval-based techniques, and fine-tuning pipelines.

Familiarity with vector databases and retrieval-augmented generation (RAG) frameworks.

Experience with cloud platforms (e.g., GCP, AWS, or Azure) for AI model deployment.

Soft Skills:

Exceptional problem-solving skills with attention to detail.

Strong communication skills to collaborate across teams and explain technical concepts to non-technical stakeholders.

Empathy and customer-centric mindset to design effective conversational solutions.

Nice to Have

Experience in reinforcement learning with human feedback (RLHF).

Familiarity with ethical AI practices and handling sensitive customer data.

Knowledge of sentiment analysis, emotion detection, and behavioral modeling in AI systems.

Perks and benefits