Senior Data Scientist

Responsibilities:

• Design, architect, and scale AI/ML and LLM-powered systems that drive personalization, assist teachers, and improve learning programs.
• This role combines hands-on data science expertise with strong engineering leadership and cross-functional ownership.
• Lead the design, development, and deployment of ML, deep learning, and LLM-based systems.
• Architect scalable AI/ML platforms, services, and workflows with strong engineering best practices.
• Own the end-to-end DS lifecycle: problem definition, experimentation, deployment, and monitoring.
• Build reproducible, well-documented model pipelines and production-grade ML systems.
• Drive model QA/QC processes, including validation, drift detection, observability, and experiment tracking.
• Lead initiatives across AI, product, and engineering teams with strong execution and agile thinking.
• Collaborate closely with product, engineering, and domain teams to define modeling strategies and roadmap priorities.
• Mentor and guide team members on ML systems, architecture decisions, and engineering excellence.
• Continuously improve system reliability, scalability, documentation, and development processes.

Key Success Factors:

• Strong ownership mindset with the ability to independently lead initiatives and teams.
• High engineering standards, clean architecture thinking, and excellent documentation practices.
• Strong mission alignment and passion for building impactful AI-driven products.
• Agile mindset with the ability to adapt, prioritize, and execute in fast-moving environments.
• Curiosity, problem-solving ability, and a bias toward shipping reliable, scalable systems.

Requirements:

• 3+ years of hands-on experience building and deploying AI/ML systems in production.
• Strong experience in applied ML, NLP, LLMs, and modern AI workflows.
• Strong fundamentals in statistics, ML algorithms, deep learning, and system design.
• Proficiency in Python and ML ecosystem tools (pandas, numpy, scikit-learn, PyTorch/TensorFlow).
• Experience with ML workflow tools such as MLflow, experiment tracking, and model registries.
• Hands-on experience with LLMs, fine-tuning, embeddings, vector databases, and RAG pipelines.
• Experience deploying models/services through APIs, cloud infrastructure, or containerized workflows.
• Strong collaboration and stakeholder management skills across product and engineering teams.