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Data Scientist Quantitative Analyst Jobs (NOW HIRING)

We are looking for a Data Scientist who can work with the developers and Data Analysts to perform analytics, develop risk and quant models around Insider Risk data. Ultimately, we want to create a ...

Principal Data Scientist - Quantitative Decision Science & Advanced Analytics Note: Fidelity will not provide immigration sponsorship for this position. Are you interested in operating as a senior ...

Principal Data Scientist - Quantitative Decision Science & Advanced Analytics Note: Fidelity will not provide immigration sponsorship for this position. Are you interested in operating as a senior ...

... Data Scientist, Quantitative Analyst, Product Developer, or related occupation. Requires Three (3) years of experience in each of the following: * Programming Languages, including Python, MATLAB, C/C ...

Data Science, Mathematics, Quantitative Analysis, Machine Learning, Business Analytics, Financial Engineering) * Entrepreneurial mindset with a strong sense of ownership - sets goals, drives outcomes ...

New

... Data Scientist, Quantitative Analyst, Product Developer, or related occupation. Requires Three (3) years of experience in each of the following: * Programming Languages, including Python, MATLAB, C/C ...

... Quantitative Analyst, Big Data Specialist, etc. DEGREE (Level Desired) Bachelor's Degree DEGREE (Focus) Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Engineering ...

... Quantitative Analyst, Big Data Specialist, etc. DEGREE (Level Desired) Bachelor's Degree DEGREE (Focus) Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Engineering ...

At least 3 years of demonstrated experience directly relevant to analytics, quantitative analysis, data science, artificial intelligence or machine learning * Proficiency with tools such as ...

... Quantitative Analyst, Big Data Specialist, etc. DEGREE (Level Desired) Bachelor's Degree DEGREE (Focus) Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Engineering ...

... Quantitative Analyst, Big Data Specialist, etc. DEGREE (Level Desired) Bachelor's Degree DEGREE (Focus) Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Engineering ...

... Quantitative Analyst, Big Data Specialist, etc. DEGREE (Level Desired) Bachelor's Degree DEGREE (Focus) Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Engineering ...

... Quantitative Analyst, Big Data Specialist, etc. DEGREE (Level Desired) Bachelor's Degree DEGREE (Focus) Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Engineering ...

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Data Scientist Quantitative Analyst information

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$29.5K

$125.5K

$194.5K

How much do data scientist quantitative analyst jobs pay per year?

As of Jul 30, 2026, the average yearly pay for data scientist quantitative analyst in the United States is $125,500.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,500.00 and $154,500.00 per year, depending on experience, location, and employer.

What does a quantitative Data Scientist do?

A quantitative Data Scientist analyzes large datasets using statistical and mathematical models to identify patterns and inform decision-making. They often use programming languages like Python or R, and tools such as SQL and machine learning algorithms, to develop predictive models and optimize processes. Strong analytical skills and knowledge of finance, economics, or related fields are typically required.

Will AI replace Data Analyst?

AI can automate routine data analysis tasks, but Data Analysts play a crucial role in interpreting complex data, making strategic decisions, and communicating insights. The role is evolving to include skills in machine learning tools and programming languages like Python or R, but human judgment remains essential for nuanced analysis and business context. Therefore, AI is more likely to augment than fully replace Data Analysts.

How much do quant data scientists make?

Quantitative data scientists typically earn between $100,000 and $200,000 annually, with experienced professionals and those in financial hubs earning higher salaries. Compensation often includes bonuses and stock options, especially in finance and hedge fund environments, and requires strong skills in programming, statistics, and financial modeling.

Is 40 too late for data science?

Data scientists and quantitative analysts can enter the field at any age, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and tools like Python or R, and building a strong portfolio. Age is less a barrier than demonstrated expertise and adaptability in this rapidly evolving field.

What is a Data Scientist Quantitative Analyst job?

A Data Scientist Quantitative Analyst combines data science and quantitative analysis to extract insights from large datasets, build predictive models, and support data-driven decision-making. They use statistical methods, machine learning, and programming skills to analyze financial markets, business performance, or customer behavior. Typically, they work in finance, tech, or consulting industries to solve complex problems and optimize strategies.

What are the typical day-to-day responsibilities of a Data Scientist Quantitative Analyst?

On a daily basis, a Data Scientist Quantitative Analyst manages and analyzes large datasets, develops predictive models, and interprets quantitative results to provide actionable insights for their organization. This role often involves collaborating with product managers, engineers, and business stakeholders to define project goals, design analytical approaches, and present findings in a clear, impactful manner. You may also be expected to automate data processes, maintain documentation, and stay updated on the latest analytical and industry trends. The work is dynamic and requires a balance of independent research and team-based problem solving.

What are the key skills and qualifications needed to thrive in the Data Scientist Quantitative Analyst position, and why are they important?

To thrive as a Data Scientist Quantitative Analyst, you need a solid background in statistics, data analysis, machine learning, and programming (often in Python or R), typically supported by an advanced degree in a quantitative field. Experience with data visualization tools, statistical modeling platforms, and database systems, as well as familiarity with industry certifications such as CFA or related analytics credentials, are highly valued. Strong problem-solving, critical thinking, and effective communication skills help professionals present complex findings and work collaboratively across departments. These abilities are crucial for transforming data into actionable business insights and supporting strategic decision-making.

More about Data Scientist Quantitative Analyst jobs
What cities are hiring for Data Scientist Quantitative Analyst jobs? Cities with the most Data Scientist Quantitative Analyst job openings:
What are the most commonly searched types of Data Scientist Quantitative Analyst jobs? The most popular types of Data Scientist Quantitative Analyst jobs are:
What job categories do people searching Data Scientist Quantitative Analyst jobs look for? The top searched job categories for Data Scientist Quantitative Analyst jobs are:
Infographic showing various Data Scientist Quantitative Analyst job openings in the United States as of July 2026, with employment types broken down into 89% Full Time, 6% Part Time, 1% Temporary, and 4% Contract. Highlights an 83% Physical, 7% Hybrid, and 10% Remote job distribution, with an average salary of $125,500 per year, or $60.3 per hour.

Quantitative Analyst

Amicis Global

Jersey City, NJ • On-site

$75 - $85/hr

Contractor

Re-posted 11 days ago


Job description

Title: Quantitative Analyst
Duration: 6+ Months
Location: Jersey City, NJ, 07311
Summary:
The Insider Risk team, in partnership with the Information Security Data Operations team, is working on a project to centralize IR data in the Cybersecurity Data Lakehouse (CyberDW). We are looking for a Data Scientist who can work with the developers and Data Analysts to perform analytics, develop risk and quant models around Insider Risk data. Ultimately, we want to create a human risk score for the Insider Risk program. This individual will be adept at ML, AI, and best practices around the new tools in the marketplace.
The Data Scientist / Data Modeler / Quantitative Analyst will play a critical role in advancing the Insider Risk program's detection, scoring, and decisioning capabilities. This role is responsible for designing, building, and continuously improving quantitative models, statistical methods, and analytical frameworks used to identify, assess, and prioritize insider risk across employees, contractors, vendors, and non‐human identities.
The role partners closely with Cyber, HR, Legal, Compliance, Anti‐Fraud, and Enterprise Information Protection to transform complex enterprise data into defensible risk signals, transparent scoring models, and executive‐level metrics that support investigations, governance, and regulatory scrutiny.
Required Skills:
1) Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Economics, Quantitative Finance, Computer Science, or a related discipline.
2) 5+ years of experience in data science, quantitative analysis, or risk modeling, preferably in financial services or regulated industries.
3) Strong experience building statistical or machine‐learning models (regression, classification, anomaly detection, clustering).
4) Proficiency in Python and/or R, with experience in SQL for large‐scale data analysis.
5) Hands‐on experience working with complex enterprise datasets and translating analytics into business decisions.
6) Strong communication skills with the ability to explain complex analytical concepts to non‐technical stakeholders.
7) Experience supporting Insider Risk, Fraud, AML, Cybersecurity, UEBA, or Threat Analytics programs.
8) Familiarity with identity and access data, endpoint telemetry, DLP, email, or collaboration monitoring.
9) Experience with model explainability, governance, and validation in regulated environments.
10) Knowledge of employee lifecycle risk, behavioral analytics, or human‐centric risk modeling.