about 4 hours ago
Responsibilities
- Lead end-to-end development of ML models for ETA, risk, anomaly, and fraud detection.
- Develop reusable ML infrastructure to scale model development and reduce time-to-production.
- Build LLM-powered solutions and establish evaluation and explainability frameworks.
- Translate customer workflows into well-defined ML problems with measurable business impact.
- Establish strong evaluation frameworks tied to business outcomes.
- Collaborate with engineering teams to build scalable, reliable systems across the ML lifecycle.
Requirements
- 8–10+ years of experience in Data Science or Applied ML with a proven track record.
- Deep expertise in tree-based models, transformers, probabilistic modeling, and feature engineering.
- Proficient in SQL and Python, with experience in modern data platforms and MLOps tooling.
- Experience with Generative AI, LLMs, and building RAG systems.
- Familiarity with distributed systems, APIs, and deployment patterns.
- Strong analytical skills for evaluating model performance and conducting root cause analysis.
- Experience handling messy, real-world data and addressing data quality issues.
- Ability to balance ML solutions with heuristics and understand architectural trade-offs.
- Clear understanding of how ML metrics translate to business outcomes.
- Strong leadership and communication skills to influence stakeholders and convey model decisions.
Tech Stack
Categories
AI & MLData Science