Data Scientist / Data Engineer, Alternative Data and AI

INTERVAL PARTNERS

Data Scientist / Data Engineer, Alternative Data and AI

1–3 Years Experience

📍

New York, NY, Midtown East, Onsite

⏱

Full-Time

✉️

[email protected]

ABOUT THE ROLE

Interval Partners is a multi-billion-dollar alternative investment firm located in Midtown Manhattan. We are seeking a Data Scientist/Engineer to join our team. This role will report to the firm’s Data Engineer/Developer and President. This is a hands-on, ownership-oriented role focused first on data engineering: building reliable pipelines, maintaining curated datasets, and making alternative data ready for analyst and portfolio manager use. The role also requires strong judgment about what each dataset measures, where its limitations are, and how LLMs, AI agents, and tool-based workflows can make analysts faster. You will work directly with portfolio managers and senior analysts on questions tied to live investment decisions, with reliable data and targeted AI solutions at the center of the work.

KEY RESPONSIBILITIES

Alternative Data Acquisition, Ingestion & Pipeline Maintenance

•      Build, maintain, and improve Python-based pipelines for ingesting, processing, and visualizing structured datasets, including alternative data such as credit/debit card transactions, point-of-sale data, and internal data assets.

•      Collaborate on ingestion workflows that are reliable, repeatable, observable, and easy to maintain, with clear treatment of schema changes, late-arriving data, vendor restatements, duplicate records, and missing values.

•      Develop automated checks for data completeness, consistency, timeliness, and accuracy, and create clear escalation paths when pipeline failures or data quality issues occur.

Data Cleaning, Transformation & Readiness

•      Transform raw data into clean, well-structured, analysis-ready outputs that map to relevant business metrics, key performance indicators, and analyst research workflows.

•      Perform data profiling, normalization, enrichment, deduplication, entity resolution, outlier handling, and quality remediation to improve downstream usability.

•      Understand the meaning, lineage, limitations, and caveats of each dataset, and clearly document assumptions, coverage gaps, definitions, and known quality constraints.

Analyst Enablement & Data Understanding

•      Work closely with analysts and portfolio managers to understand research questions, translate them into data requirements, and deliver well-documented datasets, extracts, and analyses.

•      Dig into the drivers behind trends observed in the data, helping analysts distinguish durable signals from noise, one-off effects, data artifacts, or coverage changes.

•      Surface data-driven alerts, explainable anomalies, and relevant changes in key metrics where the underlying data quality and business interpretation are well understood.

•      Communicate technical findings, data caveats, statistical context, and limitations clearly to both technical and non-technical stakeholders.

AI Readiness & Practical AI Use Cases

•      Maintain data assets in formats that can be safely and effectively used by analytics tools, LLM applications, AI agents, and retrieval or tool-based workflows.

•      Demonstrate a good conceptual understanding of large language models, AI agents, tool use, retrieval-augmented generation, embeddings, structured outputs, and prompt-driven workflows.

•      Identify practical AI-enabled use cases that improve analyst efficiency, such as conversational data exploration, automated research summaries, data quality explanations, metric lookup, and hypothesis triage.

•      Partner with technology teams to ensure that AI solutions are grounded in clean, documented, well-permissioned, and trustworthy data rather than treating AI development as the primary responsibility of the role.

Analytical Methods & Model Evaluation

•      Apply appropriate statistical and time-series techniques to support KPI forecasting, anomaly detection, trend analysis, and signal evaluation when required by analyst use cases.

•      Conduct disciplined backtesting and validation of datasets, signals, and model outputs, with attention to overfitting, data revisions, and signal stability.

•      Document model assumptions, evaluation results, confidence ranges, and limitations in a way that supports informed analyst decision-making.

QUALIFICATIONS

Required …