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Entry Level Data Analyst Fresh Graduate Jobs in Boston, MA

Company Description Data Cloud Merge is a communications Company that was established in the year ... Conduct GAP analysis to understand the AS-IS and TO-BE requirement documents * Work closely with ...

Supply Chain Analyst (Entry Level) Visa: OPT EAD, CPT, H4-EAD, and other EAD holders encouraged to ... Collect, validate, and interpret supply chain data to generate insights and recommendations.

... analyst, data specialist, database analyst, program analyst, research analyst, or research ... graduate level is strongly preferred. Applicants should submit materials including a letter of ...

Product Surveillance Analyst

Burlington, MA ยท On-site

$170K/yr

... Entry-level role Will accept fresh college graduates with internship or capstone project work ... data related to product and patient information coming in from the field Must have excellent ...

Analytical Support: Apply analytical methods and machine learning algorithms to help identify ... graduate experience); or MS with 2-4 years of experience; or BS with 4+ years of relevant ...

New

Machine Learning Analyst

Boston, MA ยท On-site

$110K - $145K/yr

We are seeking a Machine Learning Analyst to join the growing data analytics and machine learning team. The team's primary mission is to develop machine learning systems to answer open-ended ...

Machine Learning Analyst

Boston, MA ยท On-site

$110K - $145K/yr

We are seeking a Machine Learning Analyst to join the growing data analytics and machine learning team. The team's primary mission is to develop machine learning systems to answer open-ended ...

Showing results 21-40

Entry Level Data Analyst Fresh Graduate information

See Boston, MA salary details

$14

$35

$67

How much do entry level data analyst fresh graduate jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for entry level data analyst fresh graduate in Boston, MA is $35.77, according to ZipRecruiter salary data. Most workers in this role earn between $22.98 and $39.95 per hour, depending on experience, location, and employer.

What is the difference between Entry Level Data Analyst Fresh Graduate vs Data Technician?

AspectEntry Level Data Analyst Fresh GraduateData Technician
Required CredentialsBachelor's in Data Science, Statistics, or related fieldTechnical diploma or associate degree in IT or data management
Work EnvironmentOffice setting, collaborative teams, data analysis projectsData centers, IT departments, technical support environments
Employer & Industry UsageBusiness, finance, marketing, healthcareIT firms, data management companies, tech support
Common Search & ComparisonYesYes

The Entry Level Data Analyst Fresh Graduate typically focuses on analyzing data, creating reports, and supporting decision-making processes using statistical tools. In contrast, a Data Technician primarily manages data systems, maintains databases, and ensures data integrity. While both roles require technical skills, the analyst role emphasizes interpretation and insights, whereas the technician role centers on data infrastructure and technical support.

What are the most commonly searched types of Data Analyst Fresh Graduate jobs in Boston, MA? The most popular types of Data Analyst Fresh Graduate jobs in Boston, MA are:
What are popular job titles related to Entry Level Data Analyst Fresh Graduate jobs in Boston, MA? For Entry Level Data Analyst Fresh Graduate jobs in Boston, MA, the most frequently searched job titles are:
What cities near Boston, MA are hiring for Entry Level Data Analyst Fresh Graduate jobs? Cities near Boston, MA with the most Entry Level Data Analyst Fresh Graduate job openings:

PhD Graduate Intern Quantitative Portfolio Risk Analytics

RiskAnalytics

Cambridge, MA โ€ข On-site

Other

Posted 5 days ago


Job description

Ph.D. Graduate Intern โ€“ Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview

We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.

We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to applyโ€”especially those interested in translating rigorous quantitative methods into real-world financial applications.

Key Responsibilities

Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches

Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk

Design and implement analytical tools and pipelines using Python and SQL

Contribute to model validation, backtesting, and performance evaluation

Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure

Communicate complex quantitative insights through clear visualizations and technical summaries

Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics

Required Qualifications

Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines)

Strong foundation in probability, statistics, and numerical methods

Proficiency in Python (NumPy, pandas, or similar) and/or SQL

Experience working with large datasets and implementing quantitative models

Ability to think rigorously about complex systems and translate theory into practical solutions

Preferred Qualifications

Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk)

Experience with scientific computing, optimization, or machine learning

Background or research in cross-disciplinary areas such as:

Statistical physics, complex systems, or network theory

Applied or computational mathematics

Machine learning or probabilistic modeling

Quantum computing or advanced optimization techniques

Topological data analysis or geometric data methods

Prior research, publications, or project work demonstrating advanced quantitative modeling

What Youโ€™ll Gain

Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics

Opportunity to apply cutting-edge academic methods in a production environment

Collaboration with a highly quantitative, cross-disciplinary team

Experience working with large-scale financial data and modern analytics infrastructure

Mentorship and potential pathway to full-time quantitative roles

Duration & Compensation

Internship: Summer 2026, with potential to extend

Paid internship (competitive, based on experience and location)