Data Scientist

Responsibilities:

• Define AI roadmap, tooling choices, and best practices for model building, prompt engineering, fine-tuning, and vector retrieval systems
• Architect, develop and deploy large-scale ML and GenAI-powered products and pipelines
• Own all stages of the data science project lifecycle, including:
  • Identification and scoping of high-value data science and AI opportunities
  • Partnering with business leaders, domain experts, and end-users to gather requirements and align on success metrics
  • Evaluation, interpretation, and communication of results to executive stakeholders
  • Lead exploratory data analysis, proof-of-concepts, model benchmarking, and validation experiments for both ML and GenAI approaches
  • Establish and enforce coding standards, perform code reviews, and optimize data science workflows
  • Drive deployment, monitoring, and scaling strategies for models in production (including both ML and GenAI services)
  • Mentor and guide junior data scientists; foster a culture of continuous learning and innovation
  • Manage stakeholders across functions to ensure alignment and timely delivery

 

Technical Requirements:

• Hands-on experience with large language models (e.g., OpenAI, Anthropic, Llama), prompt engineering, fine-tuning/customization, and embedding-based retrieval
• Expert proficiency in Python (NumPy, Pandas, SpaCy, scikit-learn, PyTorch/TF 2, Hugging Face Transformers)
• Deep understanding of ML & Deep Learning models, including architectures for NLP (e.g., transformers), GNNs, and multimodal systems
• Strong grasp of statistics, probability, and the mathematics underpinning modern AI
• Ability to surf and synthesize current AI/ML research, with a track record of applying new methods in production
• Proven experience on at least one end-to-end GenAI or advanced NLP project: custom NER, table extraction via LLMs, Q&A systems, summarization pipelines, OCR integrations, or GNN solutions
• Familiarity with orchestration and deployment tools: Docker, Airflow, Kubernetes, Redis, Flask/Django/FastAPI, PySpark, SQL, R-Shiny/Dash/Streamlit
• Openness to evaluate and adopt emerging technologies and programming languages as needed

 

Good to have:

• Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or related field (minimum Bachelor’s)
• 2+ years of relevant experience in Data Science/AI
• Prior experience in the Economics/Financial industry, especially with market-intelligence or risk analytics products
• Public contributions or demos on GitHub, Kaggle, StackOverflow, technical blogs, or publications