
Financial services company
Quantitative Researcher, Portfolio Optimization
Job Description:
The Role
As an integral part of the Quantitative Research and Investing (QRI) group, the portfolio optimization team is responsible for research into mathematical techniques to improve the performance, net of taxes, of client portfolios and funds.
As a part of the optimization team and working closely with investment and technology teams, you will design novel mathematical approaches to solve difficult portfolio optimization problems efficiently and reliably. You will work closely with research teams to understand the breadth and scope of their optimization problems, to correctly model them in a research environment. Using your experience, you will deliver heuristics and reformulations which allow solvers to quickly converge to optimal solutions for specific classes of such problems, for both taxable and non-taxable portfolios. While your role will be focused on research, you will partner with team members to direct the implementation of your findings in the portfolio optimization system.
The Expertise and Skills You Bring:
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A Master’s degree or higher in computer science, engineering, or mathematics, with extensive coursework in optimization.
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5+ years in portfolio optimization-focused roles, researching the algorithms and mathematics that support optimization, with direct experience using general purpose solvers (Gurobi, CPLEX) or proprietary solvers (e.g. conic).
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Demonstrable track record delivering mathematical solutions to common portfolio optimization challenges, including Integer Programming, using linear algebra, and implementing heuristics for more efficient integer solutions.
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Deep understanding of portfolio construction for taxable mandates and efficient heuristics and approximation algorithms for this problem class.
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Substantial exposure to mathematical techniques specific to portfolio optimization which take advantage of the structure of portfolios, risk, and tax to find optimal solutions efficiently.
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Track record delivering optimization solutions for clients in financial services with direct experience working with clients, synthesizing their high-level needs with implementation in mathematics and working prototypes.
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Proven ability to take a research question from inception to a positive outcome independently, with appropriate scientific rigor. Sufficient fluency in technologies and tools to work independently.
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A consistent record of architecting sophisticated mathematical solutions to work efficiently and reliably in critical applications. Ability to recognize business risk and surface it to key decision-makers.
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Collaborative approach and ability to work with partners with different skills and expertise to deliver complex, reliable, scalable solutions to portfolio optimization problems.
The Value You Deliver:
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Improve numerical stability and efficiency of portfolio optimization algorithms that work at scale, across large numbers of diverse portfolios.
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Design and validate heuristics and problem reformulations to solve complex optimization problems for taxable portfolios which otherwise don’t solve to optimality.
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Independently deliver mathematical solutions and provide guidance to the team responsible for implementing them.
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Research novel algorithms to support new and expanded capabilities and specific needs of clients.
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Partner with team members to evolve your algorithms when faced with new client portfolios that don’t solve or where solutions are unstable.
The base salary range for this position is $100,000 - $200,000 per year.
Placement in the range will vary based on job responsibilities and scope, geographic location, candidate’s relevant experience, and other factors.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.
We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.
Most roles at Fidelity are Hybrid, requiring associates to work onsite every other week (all business days, M-F) in a Fidelity office. This does not apply to Remote or fully Onsite roles. Some roles may have unique onsite requirements. Please consult with your recruiter for the specific expectations for this position.
Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.
Certifications:
Category:
Investment Professionals
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关于Fidelity

Fidelity
BootstrappedFidelity Investments, formerly known as Fidelity Management & Research (FMR), owned by FMR LLC and headquartered in Boston, Massachusetts, United States, provides financial services.
1-50
员工数
Chatham
总部位置
$150B
企业估值
评价
10条评价
3.6
10条评价
工作生活平衡
3.8
薪酬
2.5
企业文化
3.7
职业发展
2.8
管理层
3.2
65%
推荐率
优点
Supportive management and employee well-being focus
Good work-life balance and flexible hours
Excellent benefits and retirement plans
缺点
Low compensation and starting salary
Limited growth opportunities
High workload and stressful during peak times
薪资范围
47个数据点
Mid/L4
Senior/L5
Mid/L4 · Data Scientist
1份报告
$133,712
年薪总额
基本工资
$102,856
股票
-
奖金
-
$133,712
$133,712
面试评价
4条评价
难度
3.0
/ 5
时长
14-28周
体验
正面 0%
中性 75%
负面 25%
面试流程
1
Application Review
2
HR Screen
3
Phone Interview
4
Background Check
5
Offer Decision
常见问题
Behavioral/STAR
Customer Service Scenarios
Financial Services Knowledge
Past Experience
Culture Fit
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