Data Scientist

Responsibilities

• Semantic Modeling: Design and maintain semantic representations (e.g., ontologies, entity relationships) to enhance structured data queryability and AI-driven reasoning
• Natural Language to Structured Query Mapping: Design and evaluate approaches that interpret natural language questions and accurately map them to structured data queries (e.g., SQL or semantic equivalents)
• Data-to-Text Interpretation: Design and evaluate techniques for interpreting tabular data and generating human-readable natural language explanations
• LLM Prompt Strategy: Assist in developing and refining prompt engineering strategies to ensure accurate, interpretable, and relevant responses from large language models
• AI Algorithm Development: Design and implement AI algorithms that optimize key system processes and improve overall product performance
• Model Evaluation and Enhancement: Continuously evaluate, refine, and improve existing AI models and algorithms to enhance accuracy, scalability, and computational efficiency
• Research & Innovation: Stay current on advancements in AI, GenAI, NLP, and machine learning, incorporating promising techniques and ideas into product solutions
• Ethics & Compliance: Ensure all model development follows data privacy, governance, and ethical AI standards throughout the lifecycle

Requirements

• Master’s degree in Computer Science, Data Science or Applied Mathematics is required; a Ph.D. is strongly preferred
• 8+ years of experience in Data Science, with hands-on work in NLP, GenAI, and large-scale AI model development
• Strong problem-solving skills and the ability to translate complex business requirements into AI research tasks and deliverables
• Excellent communication skills (English), both verbal and written, with the ability to convey complex technical concepts to cross-functional teams

Technical Expertise

• Proven track record in applying data science, machine learning, and AI methodologies in real-world scenarios
• Strong MLOps experience to help build, deploy, and maintain machine learning models in production. Familiarity with cloud platforms (e.g., AWS, GCP, Azure)
• Understanding of model versioning, monitoring, and lifecycle management
• Proficiency in Python and common AI/ML libraries (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, Langchain, Langgraph)
• Solid understanding of LLMs, RAG architectures, GraphRAG, techniques to extract knowledge from structured and unstructured data
• Experience with Agentic workflows and Agentic system design, Orchestration of LLM-powered agents

Research Execution

• Ability to define research goals, design experiments, analyze results, and iterate independently and collaboratively
• Publications or accepted papers in top AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR) in areas related to Generative AI
• Strong performance in GenAI competitions (e.g., Hugging Face Open LLM Leaderboard, AIcrowd, EvalHack, etc.)
• Participation in leading GenAI hackathons or innovation challenges (e.g., OpenAI, Hugging Face, Google, Anthropic-sponsored)
• Top rankings or meaningful contributions to Kaggle competitions focused on GenAI/NLP
• Demonstrated ability to rapidly prototype and scale GenAI solutions in applied settings (internal tools, open-source repos, agents, or copilots)

What we offer

• Competitive salary and benefits package
• Medical insurance
• Full Remote
• Collaborative and innovative work environment
• Career growth and development opportunities
• A chance to work with a talented and driven team of professionals

About the project

Our client develops a unique in-memory platform using innovative Machine Learning technologies. The product aims to help businesses’ achieve data and analytics processing needs with the highest speed, and to deliver real-time performance by reproducing companies’ data to the in-memory data store. An impressively fast-growing company that partners with the most leading enterprises from all over the world within various industries including healthcare, telecommunications, retail, etc.