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Part Time Quantitative Developer Jobs (NOW HIRING)

PNC

Quant & Model Development

Manhattan, NY · On-site

$109K - $202K/yr

Performs complex quantitative analyses and models development to support decision-making by running ... Conducts on-going communication with model owners and model developers during the course of the ...

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Part Time Quantitative Developer information

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$98K

$169.7K

$259.5K

How much do part time quantitative developer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for part time quantitative developer in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.

What is the difference between Part Time Quantitative Developer vs Part Time Quantitative Analyst?

AspectPart Time Quantitative DeveloperPart Time Quantitative Analyst
Primary FocusDeveloping and implementing trading algorithms and softwareAnalyzing financial data to inform trading strategies
Required SkillsProgramming, software development, quantitative modelingData analysis, statistical modeling, financial analysis
Work EnvironmentCollaborates with traders and developers in finance firmsWorks with traders and portfolio managers in finance firms
Common CertificationsQuantitative finance certifications (e.g., CQF)Financial analysis certifications (e.g., CFA)

While both roles involve quantitative skills in finance, Part Time Quantitative Developers focus on creating trading software and algorithms, whereas Part Time Quantitative Analysts analyze data to support trading decisions. The developer role emphasizes programming and software development, while the analyst role centers on data analysis and financial modeling.

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Part Time Quantitative Developer jobs near you
Infographic showing various Part Time Quantitative Developer job openings in the United States as of September 2026, with employment types broken down into 85% Full Time, 3% Part Time, and 12% Contract. Highlights an 79% Physical, 4% Hybrid, and 17% Remote job distribution, with an average salary of $169,729 per year, or $81.6 per hour.

Quantitative Analyst Associate (2027)

Philadelphia, PA • On-site

Philadelphia Phillies - Baseball Operations
See more jobs at Philadelphia Phillies - Baseball Operationsexternal

Part-time

This job post has expired today. Applications are no longer accepted.

Job description

Title: Quantitative Analyst Associate
Department: Baseball Research & Development
Reports to: Lead/Senior Quantitative Analyst
Status: Hourly Part-Time Seasonal
Position Overview:
As a Quantitative Analyst (QA) Associate, you help shape The Phillies Baseball Operations strategies by processing, analyzing, and interpreting large and complex data. You do more than just crunch the numbers; you carefully plan the design of your own studies by asking and answering the right questions, while also working collaboratively with other analysts and software engineers on larger projects.
Using analytical rigor, you work with your team as you mine through data and see opportunities for The Phillies to improve. After communicating the results of your studies and experiments to Baseball Operations leadership and executive staff, you collaborate with front office executives, scouts, coaches, and trainers to incorporate your findings into Phillies practices. Identifying the challenge is only half the job; you also work to figure out and implement the solution.
Responsibilities:
  • Conduct statistical research projects and manage the integration of their outputs into our proprietary tools and applications (e.g., performance projections, player valuations, draft assessments, injury analyses, etc.)
  • Communicate with front office executives, scouts, coaches, and medical staff to design and interpret statistical studies
  • Assist the rest of the QA team with their projects by providing guidance and feedback on your areas of expertise within baseball, statistics, data visualization, and programming
  • Continually enhance your knowledge of baseball and data science through reading, research, and discussion with your teammates and the rest of the front office
  • Provide input to database architecture to ensure efficient application of baseball data

Required Qualifications:
  • Deep understanding of statistics, including supervised and unsupervised learning, regularization, model assessment and selection, model inference and averaging, ensemble methods, etc.
  • Meaningful experience programming, using analytical software (Python, R, or similar), and interacting with databases
  • Proven willingness to both teach others and learn new techniques
  • Willingness to work as part of a team on complex projects
  • Proven leadership and self-direction

Preferred Qualifications:
  • Possess or are pursuing a BS, MS or PhD in Statistics or related (e.g., mathematics, physics, or ops research) or equivalent practical experience
  • 0-5+ years of relevant work experience
  • Experience drawing conclusions from data, communicating those conclusions to decision makers, and recommending actions

To be considered, all candidates must submit a response for the prompt below:
In player evaluation, some metrics are highly predictive of future performance but provide limited information about why a player will succeed or fail. Other metrics may be less predictive on their own but can help identify specific strengths, weaknesses, or opportunities for improvement.
Assume you have access to several years of professional baseball data, including traditional statistics, pitch- or play-level tracking data, scouting evaluations, player demographics, injury history, and minor-league level and park context.
In 250 words or less, describe how you would determine which information should be included in a player projection model and which information should instead be used primarily to explain, diagnose, or contextualize the projection. Discuss how you would evaluate a metric that improves historical model accuracy but may not remain stable over time, may duplicate information contained in other variables, or may be difficult to obtain consistently for all players.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.

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