2

Remote Algorithmic Trading Quant Jobs in Ridgewood, NJ

Data Scientist

New York, NY · Remote

$130K - $135K/yr

Role: Data Scientist Location: Remote, however travel might be required as per business ... algorithms from scratch. You have an advanced degree in a quantitative field, such as computer ...

Design and solve real-world software engineering and algorithmic problems to test AI reasoning ... Trade secrets or internal company or university data. * Specific client information or case details.

Senior Python Software Engineer

New York, NY · On-site +1

$150K - $250K/yr

Additionally, you'll collaborate closely with quants, traders, and research teams to bolster our ... In office M-F with 10 remote days per year Base Salary Range $150,000 - $250,000 - Salaries are ...

Oversee the development and management of products related to trading algorithms, user interfaces ... Strong analytical and quantitative skills, with the ability to translate business needs into ...

Oversee the development and management of products related to trading algorithms, user interfaces ... Strong analytical and quantitative skills, with the ability to translate business needs into ...

Senior Machine Learning Engineer (Remote)

New York, NY · On-site +1

$114K - $157K/yr

Design, research and develop state-of-the-art machine learning applications and algorithms to ... A postgraduate degree in Machine Learning, Mathematics, Computer Science, or a related quantitative ...

We host advanced investing capabilities ranging from ESG investing to multi-currency trading across ... Strong fundamentals in data structures and algorithms * Experience in successful delivery of high ...

Showing results 41-60

Remote Algorithmic Trading Quant information

See Ridgewood, NJ salary details

$53.1K

$120.6K

$198.8K

How much do remote algorithmic trading quant jobs pay per year?

As of Sep 11, 2026, the average yearly pay for remote algorithmic trading quant in Ridgewood, NJ is $120,570.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,400.00 and $154,300.00 per year, depending on experience, location, and employer.

What is a remote algorithmic trading quant?

A Remote Algorithmic Trading Quant is a quantitative analyst who develops, tests, and implements mathematical models and trading algorithms for financial markets while working off-site or from home. They analyze large datasets, identify trading opportunities, and use programming languages like Python or C++ to automate trading strategies. Their work is vital for firms seeking to gain a competitive edge through data-driven, automated trading, and being remote allows them to collaborate with global teams or firms without being physically present in a traditional office setting.

What are the key skills and qualifications needed to thrive as a remote algorithmic trading quant?

To thrive as a Remote Algorithmic Trading Quant, you need advanced quantitative skills, strong programming ability (often in Python, C++, or R), and a solid background in mathematics, statistics, or related fields—typically supported by a relevant degree. Familiarity with trading platforms, financial data feeds, and version control systems, as well as experience with backtesting frameworks, is highly valued. Exceptional problem-solving, attention to detail, and effective remote communication are crucial soft skills for success in this position. These skills and qualities enable the development, testing, and deployment of robust trading strategies in a fast-paced, data-driven environment.

What are some common challenges faced by remote algorithmic trading quants, and how can they be addressed?

Remote algorithmic trading quants often face challenges such as ensuring robust communication with team members, maintaining access to secure and reliable data feeds, and collaborating effectively across time zones. To address these, quants typically use advanced collaboration tools, participate in regular virtual meetings, and follow strict cybersecurity protocols. Building strong documentation and leveraging version-control systems like Git can also help maintain workflow efficiency and code integrity while working remotely.

What is the difference between Remote Algorithmic Trading Quant vs Remote Quantitative Analyst?

AspectRemote Algorithmic Trading QuantRemote Quantitative Analyst
CredentialsDegree in finance, computer science, or mathematics; coding skills; experience with trading algorithmsDegree in finance, economics, mathematics; statistical and analytical skills; programming knowledge
Work EnvironmentFinancial firms, hedge funds, trading firms; focus on developing and testing trading algorithmsFinancial institutions, investment firms; focus on data analysis, modeling, and risk assessment
Industry UsageCommon in trading and hedge fund industriesWidespread across finance, banking, and investment sectors

The Remote Algorithmic Trading Quant specializes in developing and implementing trading algorithms within trading firms, focusing on automation and execution strategies. In contrast, the Remote Quantitative Analyst often performs broader data analysis and modeling tasks across various financial sectors. While both roles require strong quantitative skills and programming knowledge, their primary focus and work environments differ, aligning with their specific industry functions.

What are popular job titles related to Remote Algorithmic Trading Quant jobs in Ridgewood, NJ?

For Remote Algorithmic Trading Quant jobs in Ridgewood, NJ, the most frequently searched job titles are:

What cities near Ridgewood, NJ are hiring for Remote Algorithmic Trading Quant jobs?

Cities near Ridgewood, NJ with the most Remote Algorithmic Trading Quant job openings:

Data Scientist

New York, NY • Remote

$130K - $135K/yr

Full-time

Re-posted 18 days ago


Job description

Role: Data Scientist

Location: Remote, however travel might be required as per business requirements

FTE Only

Where you’re headed

You’ll creatively use data to solve business challenges, often uncovering new and transformative opportunities along the way. Applying advanced analytics, quantitative tools, and modeling techniques, you’ll interpret and offer recommendations based on insights from the data.

Where you’ve been

You’re a problem solver, data expert, analyst, and communicator, who can create new algorithms from scratch. You have an advanced degree in a quantitative field, such as computer science, engineering, physics, statistics or applied mathematics, and have:

Job Description:

  • Familiarity with statistical techniques, data mining, hypothesis testing, and exploratory data analysis
  • Strong knowledge of programming languages with a focus on machine learning and advanced analytics (such as Python, R, Scala)
  • Experience working with large datasets, data pipelines, and relational databases
  • A collaborative mindset with the ability to communicate complex analytical concepts effectively to both technical and non‑technical stakeholders
  • Excellent problem‑solving skills with the ability to analyze issues, identify root causes, and recommend solutions quickly
  • Proven experience building, validating, and deploying machine learning models end‑to‑end in production environments 
  • Strong understanding of model performance evaluation, experiment design, and model lifecycle management 
  • Ability to translate business problems into analytical frameworks and measurable success metrics 
  • Experience working cross‑functionally with engineering, product, and business teams to deliver impact at scale

 

Good to Have:

  • Experience with Generative AI, including large language models (LLMs), prompt engineering, fine‑tuning, and retrieval‑augmented generation (RAG) 
  • Exposure to Agentic AI concepts, such as autonomous agents, tool usage, planning, memory, and multi‑agent workflows 
  • Familiarity with modern ML/AI frameworks (e.g., PyTorch, TensorFlow) and MLOps tools for deployment and monitoring 
  • Experience deploying AI solutions on cloud platforms (Azure, AWS, or GCP) with an understanding of scalability and cost considerations 
  • Knowledge of responsible AI practices, including model explainability, bias mitigation, and governance