Senior Data Scientist

Responsibilities

• Develop and deploy large-scale ML and GenAI-powered products and pipelines.
• Own all stages of the data science project lifecycle, including: develop, deploy, monitor, and scale models through the full Software Development Life Cycle into production (including both ML and GenAI services).
• Perform exploratory data analysis, proof‑of‑concepts, model benchmarking, and validation experiments for both ML and GenAI approaches.
• Partner with business leaders, domain experts, and end‑users to gather requirements and align on success metrics.
• Follow coding standards, perform code reviews, and optimize data science workflows.
• Evaluate, interpret, and communicate results to executive stakeholders.

• 5+ years of professional work within the AI space or building statistical/mathematical quantitative models in production.
• Advanced technical degree (Master and above) in Sciences, Technology, Engineering, or Mathematics.
• Strong grasp of statistics, probability, and the mathematics underpinning modern AI.
• Linear programming and optimization.
• Multi‑dimensional optimizers, such as Adam, SGD, Gradient Descent …
• Ability to adjust weights for full/partial tuning of LLMs.
• Hands‑on experience with any large language models (e.g., OpenAI, Anthropic, Llama), prompt engineering, fine‑tuning/customization, and embedding‑based retrieval.
• Intermediate to expert proficiency in Python (Num Py, Pandas, Spa Cy, scikit‑learn, PyTorch/TF 2, Hugging Face Transformers).
• Understanding of ML & Deep Learning models, including architectures for NLP (e.g., transformers), GNNs, and multimodal systems.
• Solid understanding of database structures and SQL.
• Ability to perform independent research and synthesize current AI/ML research, with a track record of applying new methods in production.
• Experience in end‑to‑end GenAI or advanced NLP projects, such as NER, table extraction, OCR integrations, or GNN solutions.
• Familiarity with orchestration and deployment tools:
  Airflow, Redis, Flask/Django/FastAPI, SQL, R‑Shiny/Dash/Streamlit.
• Public contributions or demos on Git Hub, Kaggle, Stack Overflow, technical blogs, or publications.
• Openness to evaluate and adopt emerging technologies and programming languages as needed.