Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Peppercorn Solutions
Quantitative Research & Development Intern
Wealth managers are allocating more to alternatives than ever — interval funds, BDCs, private equity, evergreen credit structures — but the tools to actually see, manage, and optimize those positions alongside public securities don’t exist yet. Advisors are patching together spreadsheets and siloed reports to do work that should be driven by computation. Peppercorn is fixing that.
We’re building an AI-native unified portfolio engine: a computation layer that brings public and private investments into a single, deterministic view and handles the math that other platforms hand-wave away. When an advisor needs to run a rebalance that simultaneously accounts for redemption windows, tax lots, liquidity constraints, and client risk targets, that’s the problem our optimizer is solving.
We’re pre-seed, Boston-based, and moving fast. This internship is a chance to work on real production infrastructure — not a demo project or a shadow role. You’ll write code that runs against live data, contribute to optimization research with direct advisor impact, and work closely with the engineering team throughout.
Work Status
- Authorization
: Candidates must be authorized to work in the U.S. for the duration of the internship. We welcome students on F-1 CPT, OPT, or STEM OPT.
- Location
: Remote. Boston-area preferred; occasional in-person collaboration welcome.
- Compensation
: Paid internship. Hourly rate commensurate with level and program; discussed during offer.
- Duration
: Summer (12 weeks, June–August) or semester-based arrangements considered.
What You’ll Do
- Own a scoped piece of the optimizer or simulation stack
— you’ll work on a defined module (not open-ended exploration), with regular check-ins to keep momentum and direction aligned.
- Research and prototype quantitative approaches
— read literature, implement algorithms, and benchmark them against existing approaches in Python; your results will directly inform production decisions.
- Contribute to our historical simulation framework
— build or extend test scenarios that stress-test portfolio optimization logic across realistic market conditions.
- Write clean, tested Python code
(Polars, NumPy, SciPy) following production standards — code review is part of the internship, and real feedback is part of the deal.
- Participate in technical design discussions
— we’re early enough that architecture decisions are still being made, and we want interns to have opinions and express them.
- Leverage AI tools to accelerate your work
— we treat AI-assisted development as a core skill.
What You Need
- Currently enrolled in an undergraduate or graduate program
in computational mathematics, financial engineering, statistics, computer science, or a closely related field; entering at least your junior year preferred.
- Coursework or project experience in quantitative or statistical methods
— optimization, ML/AI, numerical methods, or simulation. We care more about what you’ve built or studied than what job titles you’ve held.
- Working proficiency in Python
— you should be comfortable writing a script from scratch, using Polars or NumPy for data manipulation, and reading someone else’s code and understanding it.
- Foundational understanding of linear algebra, probability, and statistics
— you can reason through a covariance matrix or an objective function.
- Comfort with Git
— branching, committing, and making a pull request; no exotic workflows required.
- Intellectual honesty
— the ability to say “I don’t know yet, but here’s how I’d figure it out” is more important than having all the answers.
- Ability to leverage AI tools and platforms
to research problems, debug code, and accelerate your workflow.
- Strong written communication
— you’ll document your work and present findings; clarity matters as much as the math.
What’s Good to Have
- Exposure to mathematical optimization
— coursework or a project using LP/QP/MIP formulations; experience with Gurobi or CPLEX is a differentiator.
- Any prior finance exposure
— a course in investments, fixed income, or financial modeling; personal trading or analysis projects; familiarity with how portfolios are constructed or evaluated.
- SQL or database familiarity
— even basic SELECT queries and an understanding of relational data structures.<
Source: Peppercorn Solutions careers