Data Scientist – Equity Research

Jefferies jefferies.com Open · verified Oct 24, 2026

New York Full-time Mid-level Data Science

At a glance

Mid-level Data Science role at Jefferies. New York · full-time · $100,000–$120,000 base.

SalaryStated by Jefferies

$100,000 – $120,000

Base salary for this role, in USD per year, as published in the posting.

This role, from JefferiesOur estimate for the market: $137,581–$208,900Median $157,100

Below the market median of $157,100 for mid-level data science roles in New York.

Worked out from 54 postings that stated pay, median $157,100. Last updated Oct 23, 2026.

RoleData Science

SeniorityMid-level

LocationNew York

EmploymentFull-time

Base pay$100,000–$120,000

PostedSep 23, 2026 · 4w ago

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Query, reshape and join large datasets to answer research questions across sectors, sometimes using statistical models to forecast results
Build and maintain tools, AI agents, MCP servers and workflows that improve how the team reviews, cleans and shares data
Help bring internal and external datasets into a central platform, with AI access, quality checks, lineage and governance
Explain technical approaches to nontechnical partners and turn broad questions into evidence-based findings
Work with research teams, data vendors, management and potentially buy-side clients to maintain working relationships

What you bring

You have relevant experience on the buy-side or sell-side in data science, data research or a comparable role
A STEM degree is required, with computer science, data science, engineering, statistics or quantitative finance among the preferred fields
You bring strong Python skills, working knowledge of SQL or Apache Spark, and experience analyzing and visualizing large datasets
You have used AI-assisted coding tools and are familiar with LLMs, LangChain, vector databases and RAG pipelines

Who this fits

This role suits a mid-level data scientist who can support equity research with technical analysis and communicate clearly with nontechnical colleagues. The position is based in New York and is full time.

From the employer

Overview

The Jefferies Equity Research Data Strategy Team is a global team embedded within the Equity Research department, responsible for shaping and executing the department's data and AI strategy. On the data side, the team helps incorporate fundamental and alternative datasets into Analyst research reports, combining analysts’ domain expertise with our team’s technical skills to produce thoughtful, defensible, and actionable analysis of real-world behavior at scale. On the AI side, our team is responsible for building internal generative AI tooling along with evaluating third-party AI models and tools to enhance our analysts’ work. We help integrate AI capabilities into analysts’ daily workflows and ensure that first- and third-party datasets are fully AI-accessible to them. Statistics, data engineering, data science, and AI are cornerstones of our work.

As a Data Scientist at Jefferies, your focus will be to support the Research Analysts and the Data Strategy Team’s broader platform. The candidate will use technical and financial skills to implement ideas and requests from the Research Analysts, while also building and maintaining relationships with internal and external stakeholders, working in collaboration with the rest of the Global Data Strategy Team.

Key Responsibilities

Query, transform, analyze, and combine large datasets in support of solving problems and implementing research ideas across sectors and data categories, occasionally developing statistical models to predict outcomes

Enhance our platform through the creation and maintenance of tools, AI agents, MCP servers, and processes that streamline, scale, and standardize our ability to evaluate, clean, analyze, and disseminate data

Contribute to the data centralization process, ensuring all first- and third-party data assets are AI-accessible and meet quality, lineage, and governance standards

Present highly technical methods to non-technical stakeholders and translate open-ended questions into data-driven insights

Maintain and build relationships with key internal and external stakeholders, including research teams, third-party data vendors, management, and possibly buy-side clients

Qualifications

Required