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Risk Analytics Company
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  • Job DescriptionJob DescriptionPh.D. Graduate Intern – Quantitative Por... Read More
    Job DescriptionJob DescriptionPh.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

    Position OverviewWe are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.

    We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.

    Key ResponsibilitiesDevelop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk Design and implement analytical tools and pipelines using Python and SQL Contribute to model validation, backtesting, and performance evaluation Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure Communicate complex quantitative insights through clear visualizations and technical summaries Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics Required QualificationsCurrently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) Strong foundation in probability, statistics, and numerical methods Proficiency in Python (NumPy, pandas, or similar) and/or SQL Experience working with large datasets and implementing quantitative models Ability to think rigorously about complex systems and translate theory into practical solutions Preferred QualificationsFamiliarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) Experience with scientific computing, optimization, or machine learning Background or research in cross-disciplinary areas such as: Statistical physics, complex systems, or network theory Applied or computational mathematics Machine learning or probabilistic modeling Quantum computing or advanced optimization techniques Topological data analysis or geometric data methods Prior research, publications, or project work demonstrating advanced quantitative modeling What You’ll GainExposure to real-world portfolio risk problems at the intersection of finance and advanced analytics Opportunity to apply cutting-edge academic methods in a production environment Collaboration with a highly quantitative, cross-disciplinary team Experience working with large-scale financial data and modern analytics infrastructure Mentorship and potential pathway to full-time quantitative roles Duration & CompensationInternship: Summer 2026, with potential to extend Paid internship (competitive, based on experience and location)  Read Less
  • Operations Engineer  

    - Cambridge
    Job DescriptionJob DescriptionAbout the RoleThe company is seeking an... Read More
    Job DescriptionJob DescriptionAbout the Role
    The company is seeking an experienced Operations Engineer to join our engineering and infrastructure team.  In this role, you will be responsible for ensuring the reliable execution of operational workflows that support our financial data platforms and services. 

    The ideal candidate combines strong systems and scripting expertise with operational discipline and attention to detail.  This role works closely with engineering teams to troubleshoot issues, maintain documentation, and improve automation across our workflow ecosystem. 

    Key Responsibilities 
    Monitor and oversee the execution of scheduled operational workflowsTroubleshoot workflow issues and escalate exceptions to the Engineering team when requiredMaintain and expand the workflow knowledge base with clear technical documentationIdentify opportunities for automation and contribute to improving workflow efficiency and reliabilityEnsure accurate operational records and documentation are maintainedRequired Qualifications
    10+ years of professional experience in DevOps, Site Reliability Engineering (SRE), or closely related infrastructure/platform engineering rolesStrong proficiency with Linux operating systems in a professional environmentGeneral working proficiency with Windows systemsHands-on scripting experience with: Python, BASH and Build tools such as MakeExperience working with relational databases and SQLExperience with source control and version management, including CVS and GitFamiliarity with Windows scripting environments (Python, cmd, or PowerShell)Demonstrated diligence and attention to detail in technical documentation and operational record-keepingPreferred / Ideal Background
    Familiarity with financial data and investment portfolio workflows, including:
    Portfolio holdings and transactionsBond and equity instrumentsMarket prices and dividendsPerformance returnsPortfolio risk metricsWhat We Offer
    Opportunity to work with sophisticated financial data platformsCollaboration with experienced engineering and data teamsA technically challenging environment focused on reliability and automationCompetitive compensation and benefits 

    Read Less
  • Job DescriptionJob DescriptionPosition OverviewWe are seeking an under... Read More
    Job DescriptionJob DescriptionPosition OverviewWe are seeking an undergraduate student to support our Finance and IT teams with a focus on data analysis and quantitative problem-solving. This internship provides hands-on experience working with financial datasets, basic modeling, and tools used in data-driven decision-making.We welcome candidates from analytical and cross-disciplinary backgrounds—including mathematics, applied mathematics, statistics, economics, engineering, computer science, physics, astrophysics, quantum computing, biotech and other data-driven fields—who are interested in applying quantitative thinking to real-world business and financial problems.

    Key ResponsibilitiesWork with structured datasets to support basic financial and operational analysis Assist in organizing, cleaning, and validating data for reporting and modeling Build and maintain spreadsheets and simple analytical models in Excel Support development of reports, dashboards, and visualizations Identify patterns, inconsistencies, or trends in data Assist with automation or efficiency improvements using tools like Excel, SQL, or Python (where applicable) Collaborate with team members on data, finance, and technology-related tasks Apply quantitative or analytical approaches from coursework to practical business problems Required QualificationsCurrently pursuing a Bachelor’s degree in a quantitative or analytical field (e.g., Mathematics, Applied Mathematics, Physics, Astrophysics, Statistics, Economics, Computer Science, Quantum Computing, Engineering, Finance, Biotech, or other data-driven discipline) Strong problem-solving and analytical thinking skills Familiarity with: Microsoft Excel (formulas, basic functions) Microsoft Office (Word, PowerPoint) Comfort working with numbers and structured data Strong attention to detail and willingness to learn Preferred QualificationsExposure to programming or data tools (Python, SQL, R, or similar) through coursework or projects Experience with Excel functions (e.g., VLOOKUP, pivot tables) Introductory knowledge of statistics, probability, or data analysis Interest in financial markets, data analytics, or fintech Coursework or projects involving: Data analysis or visualizationMathematical modeling Machine learning (introductory) Computational or applied problem-solving What You’ll GainHands-on experience applying quantitative skills in a real-world business environment Exposure to financial data, analytics workflows, and decision-making processes Opportunity to build foundational data and modeling skills Mentorship and professional development Insight into career paths in quantitative finance, data science, and analytics Internship DetailsSummer 2026, with possible extension  Read Less
  • Job DescriptionJob DescriptionBenefits:401(k)Bonus based on performanc... Read More
    Job DescriptionJob DescriptionBenefits:
    401(k)Bonus based on performanceCompetitive salaryDental insuranceFlexible scheduleHealth insuranceOpportunity for advancementPaid time offVision insurance
    Senior Sales Executive / Account Executive — Risk Analytics Solutions

    Location: New York or Boston
    Reports To: Head of Sales / Chief Revenue Officer

    About the Company
    We are a leading provider of institutional risk analytics and portfolio optimization solutions serving asset managers, hedge funds, pension funds, insurers, wealth managers, and banks. Our platform delivers multi-asset risk models, performance attribution, factor analytics, stress testing, portfolio construction, and investment decision support tools used by some of the world’s most sophisticated investors.

    Our solutions help clients better understand portfolio exposures, improve investment outcomes, manage regulatory requirements, and make data-driven portfolio decisions.

    Role Overview
    We are seeking a high-performing sales professional with experience selling financial technology, analytics, or investment solutions into institutional investors. The ideal candidate understands quantitative investing workflows and has experience engaging portfolio managers, risk teams, CIOs, quantitative researchers, and investment operations leaders.

    This role is responsible for driving new business growth, expanding strategic accounts, and managing complex enterprise sales cycles for risk analytics and portfolio management solutions.

    Key Responsibilities
     Develop and execute a strategic sales plan focused on institutional asset managers, hedge funds, banks, insurers, and asset owners  Identify, qualify, and close new business opportunities for risk analytics, factor modeling, portfolio optimization, and performance attribution solutions  Build trusted relationships with CIOs, portfolio managers, heads of risk, quantitative analysts, and investment technology teams  Lead complex enterprise sales cycles from prospecting through contract negotiation and close  Collaborate with product specialists, quantitative research, customer success, and implementation teams to deliver tailored client solutions  Conduct client presentations, platform demonstrations, and business value discussions  Maintain a strong pipeline and accurate forecasting within CRM systems  Represent the company at industry conferences, client events, and thought leadership forums  Stay current on market structure, quantitative investing trends, risk management practices, and competitor offerings  Partner with marketing and product teams to provide client feedback and identify new market opportunities Required Qualifications
     Bachelor’s degree in Finance, Economics, Mathematics, Business, or related field  5+ years of enterprise sales experience in financial technology, analytics, market data, risk management, or investment software  Proven track record of exceeding revenue targets and closing complex institutional deals  Strong understanding of:  Portfolio risk analytics  Factor models and performance attribution  Quantitative investing concepts  Multi-asset portfolio construction  Institutional investment workflows  Experience selling to buy-side institutions including asset managers, hedge funds, pension funds, and insurance firms  Excellent communication, presentation, and consultative selling skills  Ability to engage both technical and executive stakeholders  Familiarity with CRM platforms such as HubSpot Preferred Qualifications
     Experience selling platforms similar to Barra, Axioma, Aladdin, FactSet, MSCI, Bloomberg, or BlackRock solutions  CFA, CAIA, or advanced finance-related coursework  Understanding of APIs, cloud-based analytics platforms, or investment data infrastructure  Existing network within institutional investment management Success Metrics
     Annual recurring revenue (ARR) growth  New logo acquisition  Enterprise account expansion  Pipeline generation and conversion rates  Client retention and strategic account development What We Offer
     Competitive base salary plus uncapped commission  Equity participation opportunity  Comprehensive healthcare and retirement benefits  Flexible work environment  Exposure to leading institutional investors and cutting-edge quantitative technologies  Career growth within a rapidly expanding analytics organization Ideal Candidate Profile
    The successful candidate combines strong commercial instincts with credibility in front of sophisticated investment professionals. They are comfortable discussing portfolio construction, risk decomposition, factor exposures, and investment workflows while also navigating enterprise procurement and executive stakeholder management.

    Read Less
  • Job DescriptionJob DescriptionPh.D. Graduate Intern – Quantitative Por... Read More
    Job DescriptionJob DescriptionPh.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)


    Position Overview
    We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.

    We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.

    Key Responsibilities
    Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk Design and implement analytical tools and pipelines using Python and SQL Contribute to model validation, backtesting, and performance evaluation Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure Communicate complex quantitative insights through clear visualizations and technical summaries Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics Required Qualifications
    Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) Strong foundation in probability, statistics, and numerical methods Proficiency in Python (NumPy, pandas, or similar) and/or SQL Experience working with large datasets and implementing quantitative models Ability to think rigorously about complex systems and translate theory into practical solutions Preferred Qualifications
    Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) Experience with scientific computing, optimization, or machine learning Background or research in cross-disciplinary areas such as: Statistical physics, complex systems, or network theory Applied or computational mathematics Machine learning or probabilistic modeling Quantum computing or advanced optimization techniques Topological data analysis or geometric data methods Prior research, publications, or project work demonstrating advanced quantitative modeling What You’ll Gain
    Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics Opportunity to apply cutting-edge academic methods in a production environment Collaboration with a highly quantitative, cross-disciplinary team Experience working with large-scale financial data and modern analytics infrastructure Mentorship and potential pathway to full-time quantitative roles Duration & Compensation
    Internship: Summer 2026, with potential to extend Paid internship (competitive, based on experience and location) 

    Read Less
  • Job DescriptionJob DescriptionPosition OverviewWe are seeking an under... Read More
    Job DescriptionJob DescriptionPosition Overview
    We are seeking an undergraduate student to support our Finance and IT teams with a focus on data analysis and quantitative problem-solving. This internship provides hands-on experience working with financial datasets, basic modeling, and tools used in data-driven decision-making.
    We welcome candidates from analytical and cross-disciplinary backgrounds—including mathematics, applied mathematics, statistics, economics, engineering, computer science, physics, astrophysics, quantum computing, biotech and other data-driven fields—who are interested in applying quantitative thinking to real-world business and financial problems.

    Key Responsibilities
    Work with structured datasets to support basic financial and operational analysis Assist in organizing, cleaning, and validating data for reporting and modeling Build and maintain spreadsheets and simple analytical models in Excel Support development of reports, dashboards, and visualizations Identify patterns, inconsistencies, or trends in data Assist with automation or efficiency improvements using tools like Excel, SQL, or Python (where applicable) Collaborate with team members on data, finance, and technology-related tasks Apply quantitative or analytical approaches from coursework to practical business problems Required Qualifications
    Currently pursuing a Bachelor’s degree in a quantitative or analytical field (e.g., Mathematics, Applied Mathematics, Physics, Astrophysics, Statistics, Economics, Computer Science, Quantum Computing, Engineering, Finance, Biotech, or other data-driven discipline) Strong problem-solving and analytical thinking skills Familiarity with: Microsoft Excel (formulas, basic functions) Microsoft Office (Word, PowerPoint) Comfort working with numbers and structured data Strong attention to detail and willingness to learn Preferred Qualifications
    Exposure to programming or data tools (Python, SQL, R, or similar) through coursework or projects Experience with Excel functions (e.g., VLOOKUP, pivot tables) Introductory knowledge of statistics, probability, or data analysis Interest in financial markets, data analytics, or fintech Coursework or projects involving: Data analysis or visualizationMathematical modeling Machine learning (introductory) Computational or applied problem-solving What You’ll Gain
    Hands-on experience applying quantitative skills in a real-world business environment Exposure to financial data, analytics workflows, and decision-making processes Opportunity to build foundational data and modeling skills Mentorship and professional development Insight into career paths in quantitative finance, data science, and analytics Internship Details
    Summer 2026, with possible extension 
    Read Less

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