Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
API @ 4
AWS @ 4
Azure @ 4
CI/CD @ 3
Cloud Computing
Docker @ 3
GenAI
Generative AI @ 3
Git @ 4
GitHub @ 4
Java @ 4
Kubernetes @ 3
LLM @ 3
Machine Learning @ 4
Python @ 4
R @ 4
React @ 4
Rust @ 4
SQL @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Bloomberg's quantitative investment and research workflows are evolving through advances in cloud computing, open-source technologies, and artificial intelligence. The Senior Client Quant Specialist team helps clients address complex investment research and enterprise workflow requirements using Bloomberg's Python-based quantitative development platform, BQuant Enterprise.
Powered by JupyterLab, BQuant Enterprise combines cloud architecture, specialized workflow libraries, and Bloomberg's financial database to help clients generate quantitative research and develop and deploy research and investment workflows at scale.
This is a highly client-facing pre-sales engineering role at the intersection of financial markets, quantitative research, and technology. You will partner with Bloomberg's BQuant Sales team throughout the client lifecycle, from technical discovery and solution design to proof-of-concept development and adoption. You will work with quantitative researchers, portfolio managers, data scientists, developers, and technology leaders to understand investment and technology needs and translate them into scalable solutions using Bloomberg's quantitative platform, data, APIs, and enterprise capabilities.
Responsibilities
- Partner with the BQuant Sales team as a technical pre-sales quant specialist to identify, qualify, and progress opportunities.
- Lead technical discovery with prospective clients to understand business needs, data requirements, and technology architecture.
- Develop proofs of concept, prototypes, and technical demonstrations using Bloomberg's quantitative platform, data, and enterprise capabilities.
- Understand clients' cloud and enterprise technology environments and demonstrate integrations with research, data, and analytics workflows across AWS, Azure, and Google Cloud.
- Collaborate with CTO and Engineering teams to translate client requirements and market trends into product feedback and enhancements.
- Stay current with emerging technologies, including generative AI, large language models, Amazon Bedrock, and Azure OpenAI.
- Work with sophisticated financial institutions across quantitative research, portfolio construction, and investment workflows involving Equities, Fixed Income/Credit, FX, Commodities, and other asset classes.
Requirements
- At least 5 years of relevant experience in pre-sales engineering, solutions engineering, technical consulting, quantitative finance, or an investment-related role.
- Front-office or buy-side experience working with quantitative researchers, portfolio managers, or investment teams.
- Knowledge of Equity, Fixed Income/Credit, or Macro quantitative investment strategies.
- Experience with Python, SQL, Git/GitHub, AI technologies, and cloud platforms such as AWS, Azure, or Google Cloud.
- Familiarity with LLM APIs and enterprise generative AI services such as Amazon Bedrock, Azure OpenAI, or similar technologies is a plus.
- Commercial and strategic mindset, with the ability to assess client needs, identify opportunities, and connect technical solutions to business value.
- Strong articulation, consultative skills, and confidence in client interactions.
- Ability to work effectively in a cross-functional environment with multiple teams and stakeholders.
Preferred Qualifications
- Experience with large-scale financial datasets and modern data platforms.
- Experience applying machine learning, AI, or advanced analytics to financial market use cases.
- Familiarity with Docker, CI/CD, Kubernetes, or other software engineering and deployment technologies.
- Knowledge of R, Java, C#, React, Rust, or other programming and analytical technologies.
- Spanish or Portuguese language skills are a plus.
Benefits
The compensation package may include merit increases, incentive compensation, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, a 401(k) match, life insurance, and wellness programs.