AI-Driven Data Scientist & Engineer — Pipelines & Insights

Interval Partners, LP; New York (NY); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Full medical & vision

401(k)

Job summary

Interval Partners, LP seeks a Data Scientist/Data Engineer focused on alternative data and AI. You will build and maintain Python-based pipelines to ingest, process, and visualize diverse datasets, including non-traditional sources.

Collaboration with analysts and PMs will shape data requirements and AI-enabled workflows. Responsibilities span data cleaning, profiling, enrichment, and ensuring data quality while enabling reliable KPI insights.

Qualifications

• Hands-on experience with Python for data engineering and analysis.
• Bachelor’s degree in a quantitative discipline.
• Ability to map raw data observations to meaningful metrics and explain data limitations.
• Familiarity with LLM tooling and applying it to analyst workflows.
• Solid grounding in statistics, time-series analysis, forecasting, anomaly detection, and backtesting.

Responsibilities

• Build, maintain, and improve Python-based data pipelines for ingesting, processing, and visualizing structured datasets, including alternative data.
• Develop automated checks for data completeness, consistency, timeliness, and accuracy with clear escalation paths.
• Transform raw data into analysis-ready outputs and document assumptions, coverage gaps, and quality constraints.

Skills

Python data engineering

Pandas

NumPy

LLM tooling

Time-series analysis

Data validation

Communication

Dataset management

Education

Bachelor's degree in Computer Science / Data Engineering / Statistics

Tools

Pandas

NumPy

Job description

Interval Partners, LP seeks a Data Scientist/Data Engineer focused on alternative data and AI. You will build and maintain Python-based pipelines to ingest, process, and visualize diverse datasets, including non-traditional sources.

Collaboration with analysts and PMs will shape data requirements and AI-enabled workflows. Responsibilities span data cleaning, profiling, enrichment, and ensuring data quality while enabling reliable KPI insights.