We are looking for experienced and passionate Data Scientists to join our dynamic consultancy team, delivering cutting-edge Artificial Intelligence solutions across a wide range of industries including retail, banking, telecom and airlines.

In this role, you will be responsible for designing and developing predictive and forecasting models using both classical statistical methods and modern machine learning techniques, while also contributing to LLM-based AI solutions.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field
• 2+ years of experience in data science or applied machine learning with a focus on predictive analytics or forecasting
• Experience with demand forecasting, financial time series or capacity planning projects
• Proficient in Python or R and experienced with commonly used data science libraries and frameworks
• Strong command of SQL for data extraction, transformation, and analysis
• Hands-on experience with LLMs and NLP libraries (HuggingFace, LangChain, OpenAI API)
• Familiar with MLOps tools like MLflow, Docker, or Kubernetes
• Comfortable communicating with clients, understanding business needs, and presenting results to both technical and non-technical audiences
• Strong verbal and written communication skills in English

Key Responsibilities

• Design and implementation of time series forecasting models (e.g. ARIMA, Prophet, LSTM, XGBoost) for demand prediction, sales forecasting and resource planning
• Lead the development and fine-tuning of LLM-based solutions for NLP tasks including text summarization, classification, conversational AI, and RAG (Retrieval-Augmented Generation)
• Conduct end-to-end data science workflows, including data cleaning, feature selection, transformation, and exploratory data analysis
• Collaborate with clients and stakeholders to understand business requirements and present data-driven insights clearly.
• Stay up-to-date with the latest trends in AI, ML, and LLM technologies and proactively bring them into projects
• Support the deployment of models into production environments in collaboration with engineering teams