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Remote Hedge Fund Python Jobs in New York (NOW HIRING)

De Circle

Rengo AI - AI Engineer

New York, NY · On-site +1

$125K - $150K/yr

Rengo AI is building the intelligence layer for fund management - starting with next-generation ... Strong Python (mandatory) * Experience building production data systems or analytics platforms LLM ...

Gelber Group

Experienced Energy Trader

New York, NY · On-site +1

$72K - $120K/yr

... remote candidates. What you'll do: * Manage your own trading book with a focus on energy markets ... Prior Proprietary Trading or Hedge Fund PM experience strongly preferred * Larger bank or physical ...

Gelber Group

Experienced Energy Trader

White Plains, NY · On-site +1

$72K - $120K/yr

... remote candidates. What you'll do: * Manage your own trading book with a focus on energy markets ... Prior Proprietary Trading or Hedge Fund PM experience strongly preferred * Larger bank or physical ...

ETFbook

ETF Account Manager

New York, NY · Remote

Remote - New York State, USA Employment Type: Full-time W-2 employee via an Employer of Record (EOR ... issuers, index providers, fund service providers, market makers, exchanges, and buy-side ...

New

Daloopa

Account Executive - New Business

New York, NY · On-site +1

$90K - $125K/yr

New York City (Hybrid office/remote schedule) Compensation: $90k-$125k base + commission with a 50 ... hedge funds, asset managers, investment banks, etc) * Ability to identify who will be part of the ...

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All JobsHedge Fund Python JobsRemote Hedge Fund Python JobsRemote Hedge Fund Python Jobs in New York

Remote Hedge Fund Python information

How does a remote hedge fund Python developer typically collaborate with portfolio managers and data analysts?

As a Remote Hedge Fund Python Developer, you will often work closely with portfolio managers and data analysts to understand their requirements for data processing, quantitative modeling, and automation. Collaboration usually happens through virtual meetings, code reviews, and shared documentation. Your day-to-day responsibilities may include building and maintaining data pipelines, developing tools for backtesting trading strategies, and integrating analytics into existing platforms. Effective communication and proactive problem-solving are essential, as you may need to translate complex financial concepts into scalable, efficient code while ensuring alignment with the broader investment team's objectives.

What are the key skills and qualifications needed to thrive as a remote hedge fund Python developer, and why are they important?

To thrive as a Remote Hedge Fund Python Developer, you need advanced Python programming skills, a strong understanding of financial markets, and typically a degree in computer science, mathematics, or a related field. Familiarity with quantitative analysis tools, version control systems like Git, and experience with databases and APIs are highly valuable, as are certifications in finance or data science. Excellent problem-solving abilities, attention to detail, and strong communication skills enable effective collaboration and adaptation to fast-changing requirements. These skills are crucial for delivering robust, efficient trading solutions and ensuring seamless teamwork in a high-stakes, remote financial environment.

What is the difference between Remote Hedge Fund Python vs Quantitative Analyst?

AspectRemote Hedge Fund PythonQuantitative Analyst
Required CredentialsPython programming, finance knowledge, possibly CFA or similarMathematics, statistics, finance, often advanced degrees
Work EnvironmentRemote, collaborative with hedge fund teamsOffice or remote, research-focused
Industry UsageHedge funds, asset managementInvestment firms, banks, hedge funds
Common Search/ComparisonYesYes

Remote Hedge Fund Python roles focus on developing trading algorithms and data analysis using Python within hedge funds. Quantitative Analysts also analyze financial data but often require advanced degrees in math or statistics and may work in various financial institutions. While both roles involve data analysis and finance, the Hedge Fund Python position emphasizes programming skills, whereas Quantitative Analysts focus more on mathematical modeling.

What does a remote hedge fund Python developer do?

A Remote Hedge Fund Python Developer is a software engineer who specializes in using Python to build, maintain, and optimize financial applications for hedge funds, all while working remotely. Their work often involves developing trading algorithms, analyzing large financial datasets, automating workflows, and integrating with financial APIs. They collaborate with quantitative analysts, traders, and other developers to ensure the hedge fund’s technological needs are met efficiently and securely. Being remote, they use digital collaboration tools to communicate and manage their tasks.
What are the most commonly searched types of Hedge Fund Python jobs in New York? The most popular types of Hedge Fund Python jobs in New York are:
What are popular job titles related to Remote Hedge Fund Python jobs in New York? For Remote Hedge Fund Python jobs in New York, the most frequently searched job titles are:
What job categories do people searching Remote Hedge Fund Python jobs in New York look for? The top searched job categories for Remote Hedge Fund Python jobs in New York are:
What cities in New York are hiring for Remote Hedge Fund Python jobs? Cities in New York with the most Remote Hedge Fund Python job openings:
Remote Hedge Fund Python jobs near you
Infographic showing various Remote Hedge Fund Python job openings in New York as of July 2026, with employment types broken down into 100% Part Time. Highlights an 100% Remote job distribution.

Rengo AI - AI Engineer

De Circle

New York, NY • On-site, Remote

$125K - $150K/yr

Full-time

Re-posted 18 days ago

Job description

Rengo AI is building the intelligence layer for fund management - starting with next-generation portfolio monitoring systems for investment teams.
Today, portfolio monitoring is fragmented across dashboards, spreadsheets, internal tools, and manual analyst workflows. Rengo replaces this with an AI-native monitoring layer that continuously interprets portfolio activity, risk, exposure, and performance across assets and strategies.
The Role
As a Founding AI Engineer, you will build the core system that powers AI-driven portfolio monitoring for institutional investors.
You will design systems that continuously:
  • ingest portfolio + market + position-level data
  • detect meaningful changes and anomalies
  • generate structured investment insights
  • explain performance and risk drivers in natural language + structured outputs

This is a high-reliability AI system, not a chatbot.
What You'll Build
1. AI Portfolio Monitoring Engine
  • Real-time and batch systems that monitor:
    • portfolio performance (PnL, attribution, drawdowns)
    • exposure shifts (sector, geography, asset class)
    • risk signals (volatility, correlation, concentration)
    • position-level changes
  • AI layer that converts raw portfolio data into:
    • alerts
    • summaries
    • explanations
    • actionable insights

2. Change Detection & Intelligence Layer
  • Build systems that detect:
    • significant portfolio movements
    • abnormal price/volume behavior in holdings
    • drift from target allocations
    • risk regime changes
  • Prioritization layer: what matters vs noise

3. AI-Generated Portfolio Narratives
  • Generate structured outputs such as:
    • daily / weekly portfolio reports
    • performance explanations ("why did we lose/gain?")
    • exposure breakdowns
    • risk commentary
  • Ensure outputs are:
    • auditable
    • grounded in data
    • consistent across runs

4. Data + Retrieval Systems for Funds
  • Integrate:
    • positions & holdings data
    • market data feeds
    • internal fund metadata
    • external news & filings (optional enrichment layer)
  • Build RAG pipelines over portfolio + market context

5. LLM Systems for Financial Reliability
  • Design LLM pipelines that:
    • avoid hallucinated financial reasoning
    • produce structured, verifiable outputs
    • ground insights in actual portfolio data
  • Build evaluation frameworks for correctness of financial narratives

Strong engineering background
  • 3-7+ years in backend, data engineering, or ML systems
  • Strong Python (mandatory)
  • Experience building production data systems or analytics platforms
LLM / AI systems experience
  • Experience building LLM applications in production
  • Strong understanding of:
    • RAG systems
    • structured generation (schemas, JSON outputs)
    • tool use / function calling
    • agent workflows
  • Awareness of failure modes in LLM reasoning (critical in finance)
Data-heavy systems mindset
  • Experience with:
    • time-series data
    • event-driven pipelines
    • analytics / observability systems
  • Comfort working with imperfect, high-volume financial data
Nice to Have
  • Experience in:
    • asset management / hedge funds / fintech
    • portfolio analytics or risk systems
    • trading / market data infrastructure
  • Familiarity with:
    • exposure/risk models
    • PnL attribution systems
    • BI / analytics platforms for finance
  • Experience with vector databases or hybrid retrieval systems

What Makes This Role Unique
  • You are building the core monitoring brain of a fund
  • Not dashboards - interpretation + intelligence
  • Systems you build directly influence investment decisions and risk awareness
  • High emphasis on:
    • correctness
    • traceability
    • reliability under uncertainty
  • You own the full stack: data → intelligence → insight delivery

Tech Direction
  • Python (core systems + AI orchestration)
  • LLM APIs (OpenAI / Anthropic / open-source models)
  • Postgres + time-series storage
  • Vector DB for semantic retrieval
  • Stream/batch processing pipelines
  • Cloud infrastructure (AWS/GCP)

Why Join
  • Define how AI monitors institutional portfolios
  • Replace manual analyst workflows with automated intelligence systems
  • Work on one of the hardest AI problems in finance: turning data into trustworthy interpretation
  • High ownership, early-stage, no legacy constraints

Apply