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Quant Developer Virtual Jobs in Texas (NOW HIRING)

... Engineering, Research, Data Engineering, Marketing, Sales, Finance and others. You will use data ... Apply technical expertise with quantitative analysis, experimentation, data mining, and the ...

You will partner with operational, data science, data engineering, and product teams globally to ... Bachelor's degree in a quantitative discipline such as Computer Science, Data Science, Statistics ...

Partner with Engineering, Data Science, and other teams to identify and use the data needed to ... Ability to collaborate as part of a virtual team and also work independently, including blending of ...

* Partner with Regional Sales Managers on customer meetings, virtual calls, site visits, and ... Build strong working relationships with Engineering to remain current on product improvements, new ...

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Quant Developer Virtual information

What is a quant developer virtual?

Quant Developer Virtual roles refer to remote or online positions where professionals design, develop, and maintain quantitative models and algorithms, typically for financial institutions or trading firms. These developers use programming languages like Python, C++, or Java to implement mathematical models for pricing, risk management, and trading strategies. Working virtually, they collaborate with teams through digital platforms and are often responsible for analyzing large datasets and optimizing code performance. This role requires both strong programming skills and a solid foundation in quantitative finance or mathematics.

What skills and qualifications are needed to thrive as a quant developer virtual?

To thrive as a Quant Developer Virtual, you need a strong background in mathematics, statistics, programming (especially Python, C++, or Java), and a relevant degree such as computer science, engineering, or quantitative finance. Familiarity with financial modeling tools, statistical analysis software, and version control systems like Git is typically expected. Exceptional problem-solving, analytical thinking, and effective remote communication skills set top candidates apart. These skills are crucial for developing robust quantitative models and collaborating effectively in distributed financial teams.

What are common challenges faced by quant developers working in a virtual or remote environment?

Quant Developers working virtually often face challenges in seamless collaboration, especially when discussing complex mathematical models or code structures. Effective communication is crucial, as much of the work involves frequent coordination with traders, data scientists, and other developers. To overcome these hurdles, many teams use collaborative tools, version control systems, and regular video meetings. It's also important for remote Quant Developers to proactively stay updated on project requirements and maintain clear documentation to ensure team alignment.

What is the difference between Quant Developer Virtual vs Quant Analyst?

AspectQuant Developer VirtualQuant Analyst
Required CredentialsDegree in Math, Finance, or Computer Science; programming skillsDegree in Finance, Economics, or Math; strong analytical skills
Work EnvironmentRemote, collaborative with developers and tradersOffice or remote, focused on data analysis and modeling
Industry UsageFinancial firms, hedge funds, banksAsset management, hedge funds, investment banks
Common Search/ComparisonYesYes

The main difference between a Quant Developer Virtual and a Quant Analyst lies in their focus and responsibilities. Quant Developers Virtual primarily build and implement trading algorithms and software, requiring strong programming skills. Quant Analysts focus on analyzing data, developing models, and providing insights for investment decisions. Both roles are essential in finance but differ in technical depth and daily tasks.

What are the most commonly searched types of Quant Developer jobs in Texas?

The most popular types of Quant Developer jobs in Texas are:

What cities in Texas are hiring for Quant Developer Virtual jobs?

Cities in Texas with the most Quant Developer Virtual job openings:

Consultant Machine Learning & Knowledge Graph Engineer

Round Rock, TX • On-site

Dell, Inc.
Computer and Computer Peripheral Equipment and Software Wholesalers • 10K+ employees

Full-time

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


Job description


Consultant Machine Learning & Knowledge Graph Engineer
Data Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods and models. What's more, we are in collaboration with leading academics, industry experts and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.
Join us to do the best work of your career and make a profound impact as Consultant ML & KG Engineer on our growing and dynamic team in Round Rock, Texas.
What you'll achieve
Lead the architecture, development, and deployment of enterprise scale ML solutions across Dell's global ecosystem. Drive MLOps standards, build production grade ML services, and collaborate across engineering, product, and platform teams to enable AI at scale.scale ML solutions across Dell's global ecosystem. As a Consultant Machine Learning & Knowledge Graph Engineer, you will play a pivotal role in advancing our AI and ML capabilities and creating Enterprise wide KG marketplace and Ontology layouts. You will be responsible for designing, building, and operationalizing machine learning systems, including next generation agentic and GenAI powered applications. You will drive and execute our broader AI/ML strategy. You will also be responsible to architect production-grade Knowledge Graph platforms, design semantic data layers that power Agentic AI, and drive the convergence of graph technologies with large-scale data engineering ecosystems. This role demands a rare combination of deep graph expertise, distributed systems mastery, and strategic business influence. You will work deeply across data pipelines, model development, optimization, and production deployment to deliver scalable, high performance ML solutions.
You will
  • Lead the end-to-end Agentic lifecycle-from conceptualizing, prototyping and driving delivery with engineering teams and design and build autonomous AI agents, ML systems, pipelines, and inference services.
  • Work with business leads to imagine agentic products and drive accelerated delivery through Spec Driven Development and implement MLOps practices including CI/CD, model monitoring, drift detection, and automated retraining.
  • Collaborate with Data Engineering and Platform teams to ensure data, infrastructure, and governance readiness along with providing technical leadership while integrating emerging AI/ML technologies and managing production incidents.
  • Design, build, and scale enterprise Knowledge Graph platforms using Neo4j and/or Stardog, establishing graph-native data models that enable entity resolution, relationship discovery, and semantic reasoning across business domains.
  • Define and govern enterprise ontologies (OWL 2), taxonomies, and semantic schemas that provide a unified, machine-interpretable view of Dell's data assets, ensuring consistency, reusability, and inferencing capability
  • Architect graph-backed Retrieval-Augmented Generation (RAG) systems, tool-calling interfaces, and dynamic prompt-to-graph query pipelines that fuel autonomous AI agent decision-making with deterministic, explainable knowledge

Take the First Step Towards Your Dream Career
Every Dell Technologies team member brings something unique to the table. Here's what we are looking for with this role:
Essential Requirements:
  • 12+ years of experience delivering complex AI/ML or applied science systems, including deep learning, machine learning, and LLM-based solutions.
  • Advanced Python expertise with strong knowledge of ETL pipelines (Airflow preferred) and modern data-warehousing concepts.
  • Graph Architecture Mastery: Extensive hands-on experience designing and operating production-grade graph systems using Neo4j (Cypher, GDS, APOC, AuraDB, Causal Clustering) and/or Stardog (SPARQL, OWL 2 reasoning, Virtual Graphs, SHACL validation)
  • Distributed Systems and Data Scale: Expert-level command over PySpark, Kafka, data lakehouses (Apache Iceberg, Delta Lake), and enterprise orchestration (Airflow), with proven ability to integrate these with graph ecosystems
  • Strong software engineering background with hands-on experience in AI frameworks, cloud environments, and domains such as ML, NLP, IR, recommender systems, and LLMs and proven experience with Docker, Kubernetes, and major cloud platforms (AWS/GCP/Azure), including training, fine-tuning, and applying LLMs for agentic AI applications.

Desirable Requirements
  • PhD or Master's degree in Technology, Computer Science, Machine Learning or equivalent quantitative field
  • Familiarity leveraging graph-based techniques, semantic search, hybrid search systems, and implementing solutions that combine traditional IR methods with machine learning models to enhance search relevancy accuracy and efficiency. Familiarity with large scale data handling when dealing with telemetry systems.

DELL logo

About DELL

Sourced by ZipRecruiter

Dell Technologies helps organizations and individuals build a brighter digital tomorrow. Our company is made up of more than 150,000 people, located in over 180 locations around the world. We're proud to be a diverse and inclusive team and have an endless passion for our mission to drive human progress.

Industry

Computer and computer peripheral equipment and software wholesalers

Company size

10,000+ Employees

Headquarters location

Round Rock, TX, US

Year founded

1984