Data Analyst, AI & Data Quality (Hybrid, NYC)

Hunter Bond; New York (NY); Hybride; USD 180 000 - 220 000; Plein temps

Avantages offerts par ce poste

Hybrid work model

Competitive salary

Discretionary bonus

Résumé du poste

Hunter Bond in New York is seeking a Data Analyst who thrives on messy data and works at the intersection of data engineering, analytics and applied AI. This role focuses on shaping data rather than just producing dashboards.

You’ll use Python and SQL to transform large datasets, design data pipelines from external sources, and prototype with ML/LLMs to extract value before turning experiments into production-ready processes.

Qualifications

• Master's or PhD in Computer Science, Engineering, Mathematics, Statistics, Physics, Economics or another quantitative discipline.
• Strong Python and SQL skills, developed professionally, academically or through personal projects.
• Experience with large, inconsistent or imperfect datasets and applying ML/NLP/LLMs.

Responsabilités

• Working with Python and SQL to transform and interrogate large datasets.
• Helping build and maintain data pipelines from external sources into internal systems.
• Finding data inconsistencies and tracing root causes.
• Designing checks to catch bad or unexpected data before it reaches downstream users.
• Experimenting with LLMs and machine learning to extract information from documents and unstructured sources.
• Building prototypes, evaluating approaches and measuring outcomes.
• Turning successful AI experiments into dependable production processes.
• Bringing new datasets into the business by mapping fields and integration requirements.

Connaissances

Python

SQL

Formation

Master's or PhD in quantitative field

Description du poste

Hunter Bond in New York is seeking a Data Analyst who thrives on messy data and works at the intersection of data engineering, analytics and applied AI. This role focuses on shaping data rather than just producing dashboards.

You’ll use Python and SQL to transform large datasets, design data pipelines from external sources, and prototype with ML/LLMs to extract value before turning experiments into production-ready processes.