Data Scientist

Role & responsibilities

1. Data Management & Pre-processing

- Collect, clean, and pre-process large datasets from diverse sources, ensuring high data quality. - Analyse unstructured and structured data to derive actionable insights.

2. Predictive Modelling & Machine Learning

- Develop and implement predictive models tailored to business objectives.

- Integrate machine-learning techniques with LLMs to enhance model performance.

3. Specialization in Large Language Models (LLMs)

- Fine-tune and customize LLMs (e.g., GPT, Google Gemini) for specific business needs.

- Develop prompt engineering strategies to improve model responses and outcomes.

- Collaborate with engineering teams to integrate LLMs into existing systems and workflows.

- Explore opportunities to enhance hospitality-specific use cases through advanced natural language processing (NLP) techniques.

4. Data Analysis & Insights

- Perform deep analysis to uncover trends, patterns, and key insights.

- Create forecasting tools to predict customer behaviour, operational metrics, and future trends.

5. Collaboration & Communication

- Work closely with cross-functional teams to understand business challenges and provide AI-driven solutions.

- Communicate findings through clear, impactful reports, dashboards, and presentations.

6. Innovation & Continuous Learning

- Stay updated with the latest advancements in LLMs, AI, and machine learning.

- Pilot and recommend new tools, algorithms, and frameworks to improve data science initiatives

Preferred candidate profile

Expertise in Large Language Models:

- Hands-on experience with GPT-4, ChatGPT, Google Gemini, and similar LLMs.

- Strong understanding of LLM fine-tuning, deployment, and performance optimization.

Prompt Engineering:

- Proficiency in crafting effective prompts to enhance model outputs.

Programming & Tools:

- Strong coding skills in Python, R, or similar languages.

- Familiarity with frameworks such as Tensor Flow, PyTorch, Hugging Face, or OpenAI API.

Data Management:

Machine Learning & NLP:

- Proficiency in supervised and unsupervised learning techniques.

- Advanced knowledge of natural language processing and generation.