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VP, Quant Optimization Technology
VP, Quant Optimization TechnologyFidelity Investments • Boston, MA, United States
VP, Quant Optimization Technology

VP, Quant Optimization Technology

Fidelity Investments • Boston, MA, United States
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Join to apply for the VP, Quant Optimization Technology role at Fidelity Investments

We are seeking a VP, Quant Optimization Technology to join our Quantitative Research & Investments Technology (QRIT) team in Fidelity Asset Management Technology. This role will partner with the quant research and investments teams to design, build, enhance, and support a comprehensive portfolio optimization & back testing ecosystem. The individual will combine hands-on development with leading a team of software engineers and quantitative developers. The role reports to the Head of QRIT and joins the QRIT leadership team.

Responsibilities

  • Design, develop, and support portfolio optimization and back testing systems.
  • Lead and manage a team of software engineers and quantitative developers.
  • Collaborate with research and investment teams to meet their technical needs.
  • Ensure systems are scalable, low latency, and reliable.

Qualifications

  • Bachelor’s degree in Computer Science or related field; Master’s preferred.
  • At least 10 years of professional experience in financial services, preferably in Asset Management.
  • Experience managing development teams and engaging with senior stakeholders.
  • Proficiency in Python, micro-services, RESTful APIs, AWS, CI / CD, and DevOps.
  • Strong background in system architecture, data structures, and software engineering fundamentals.
  • Experience with portfolio optimization tools.

    • Additional Skills

  • Excellent communication and interpersonal skills.
  • Ability to capture requirements and translate them into technical solutions.

    • Benefits and Culture

    Fidelity offers a hybrid working model, combining onsite and remote work. Most roles require onsite presence every other week. We value diversity, collaboration, and continuous learning.

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