Data Science/Machine Learning Engineer / Lead

The Team

What’s in it for you:

• Be a part of a global company and build solutions at enterprise scale
• Lead and grow a highly skilled, hands-on technical team (including mentoring junior data scientists)
• Contribute to solving high-complexity, high-impact problems end-to-end
• Architect and oversee production-ready pipelines from ideation to deployment

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)
• 5+ years of relevant experience in Data Science/AI, with at least 2 years in a leadership or technical lead role
• 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