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# Sector Data Analyst

- **Company:** JOB POINT
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Data Integrity, Python (Programming Language), SQL Databases, Unstructured Data, Data Processing, Apache Spark, Pandas, Information Technology, Data Analytics
- **Published:** October 20, 2026
- **Apply:** https://www.juju.com/job/23_0AD4B0CECAF65F36D9BCA3623988A063

## About the Role

Bachelor's degree or higher in Mathematics, Statistics, Physics, Computer Science, or other STEM related field; Extensive experience working with large, unstructured datasets and ideating unique features; Knowledge of general finance and company fundamentals; 3+ years in a data scientist position or as a data-driven sector analyst in finance; Working proficiency in Python or R; Proficiency in data analysis, data manipulation, and working with large unstructured datasets. Experience with SQL, Spark, Pandas, or other data analysis libraries; Strong mathematical and statistical skills

## Description

Sector Data Analyst About our Client and the Role Our client is a $20b systematic hedge fund looking to hire a sector data analyst/data scientist to focus on generating interesting insights for healthcare, energy, or industrial sector stocks from fundamental data, alternative datasets, and other unstructured data. Your work will augment fundamental analysts, and help drive investment decisions. You will also work alongside quantitative researchers and portfolio managers to help build predictive models from alternative data for systematic strategies. Responsibilities

Generate insights and identify trends from alternative datasets; Project key performance indicators (KPIs) for various public companies in the healthcare, energy, or industrial sectors; Work with other data scientists and quantitative researchers to identify and develop features useful for building predictive models; Present research to portfolio managers, investment teams, and other internal users; Stay abreast of alternative data developments and identify potential new data sources and vendors; Collaborate with engineering and data sourcing teams to vet data vendors, ensure data integrity and quality