About Peregrine

Peregrine Advisors is a rapidly growing AI and data innovation hub helping government solve hard problems and deliver better outcomes. We work across the full lifecycle—from strategy and governance through architecture, engineering, analytics, AI, visualization, and training—for clients including the SEC, PCAOB, DDOT, and DCAA.

Our clients describe our support as “absolutely excellent” and our people as “instrumental to organization’s success.” Winner of the United Nations COP27 Datathon, our work has earned all Exceptional CPARS ratings, SEC Chair’s Awards, the SEC Vision Award, and recognition from FDA leadership.

We are also built differently. Peregrine is the first and only social enterprise organized as a Benefit Corporation and SBA 8(a) company focused on AI and data for government. We believe business success should create client value, social value, and opportunity for our people.

As we enter our next stage of growth, we need dependable, motivated, and skilled people who have a combination of intellectual curiosity and intellectual courage. With new clients, larger programs, and expanding responsibilities, the people joining Peregrine now will have the opportunity to help shape the company we become.

People first. Ownership always.

We solve problems through people, process, and technology—in that order. Technology matters, but people make transformation happen. That principle defines our culture. Trust and motivation come before competence. We hire people we can trust, who care deeply about what they do, take ownership, learn quickly, help one another, and refuse to walk past a problem because it belongs to someone else.

At Peregrine, you are not joining simply to fill a position. You are joining a team, taking ownership of a mission, and helping build a company. If that excites you, we should talk.

At Peregrine Advisors, you will carry analytical questions for a federal agency from the data to a defensible, documented answer. But we hire people, not seats. As the work evolves, you will move to where the hardest problems are.

This is a remote position, although a hybrid arrangement based in the Washington, DC metropolitan area with openness to a variable commuting cadence is preferred. Wherever you work from, occasional travel to DC is part of the job. The initial engagement requires United States citizenship and the ability to obtain a Public Trust determination.

Your first project

Your first project will likely have you transforming client data, structured, semi-structured, and unstructured, into analytic processes and outcomes that help drive mission-critical initiatives. Depending on the assignment, you may:

Frame analytical questions with the people who own the mission, and turn them into designs that can actually be tested.
Build analyses and models in Python and Structured Query Language (SQL) on cloud-native data platforms hosted in Amazon Web Services (AWS).
Build models that earn their place: forecasting, classification, risk scoring, and anomaly detection matched to the question, the data, and the scrutiny a federal decision has to survive.
Design the evaluation that shows a model is fit for a client environment, with baselines, validation designs, error analysis, documented limits, and documentation that lets someone else verify your results.
Build analytical features and datasets from large, messy, document-heavy corpora, including text extracted with Amazon Textract and analyzed with foundation models through Amazon Bedrock.
Harden one-off notebooks into repeatable, reviewed analytical pipelines.
Put findings in front of decision-makers in plain language, with visuals that carry the argument instead of decorating it.
Use artificial intelligence (AI)-assisted analysis as standard practice, with language models and coding assistants across the lifecycle and your judgment on top.

Adaptability and a demonstrated capacity to learn new concepts, systems, and tools are what keep you ahead of program needs as projects complete and priorities shift.

Who you are

You are rigorous about method and honest about uncertainty: when the evidence supports a narrower claim, you make the narrower claim. You care what the data is for, not just what it contains, and you would rather spend a day understanding the process that produced a number than a week modeling it wrong. You experiment, fail, learn, and repeat quickly. You would rather own an outcome than be handed a task.

What you bring

A working command of statistics and machine learning applied to real operational data, not just curated coursework sets.
The engineering habits that make analysis trustworthy: version control, reproducible pipelines, tested transformations, and documentation someone else can follow.
The communication craft

Data Scientist, Python and AWS at OpenDataJobs