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We are seeking a skilled Quantitative Developer to support our investment research and portfolio management team. The role focuses on building and maintaining robust research pipelines, ensuring coding best practices, and delivering scalable, production grade implementation of quantitative models. This is a hands-on development role requiring close collaboration with quantitative researchers and portfolio managers.

• Build, maintain, and optimize data pipelines for financial and economic datasets
• Enforce best practices in coding, testing, and version control (e.g., Git, CI/CD workflows)
• Write unit tests and documentation to ensure model reliability and transparency
• Familiarity with financial markets and portfolio analytics
• Partner with quantitative researcher to understand the model requirements and improve research workflow
• Data management: Proper implementation of data models, pipelines, warehouse designs, and ETL principles in partnership with Technology team
• Data Quality Assurance and Production Model Experience: Ensuring information received and delivered is accurate and timely. Partner with Technology team to ensure production code is written efficiently and is run with proper oversight

Qualifications

• Bachelor’s or Master’s Degree in Computer Science, Engineering or related quantitative field with 4-6 years of work experience
• Progression towards professional certifications like CFA, FRM, CQF
• Experience in development of data pipeline in a systematic investment team
• Som exposure on assessing data quality
• Excellent programming skills in Python, experience in writing complex / advanced SQL queries and strong exposure to cloud environments like AWS
• Good understanding of version control systems like Git, SVN

Skills That Will Help You Stand Out

• Quantitative development experience
• Some exposure working with APIs
• Strong organizational, interpersonal, and proven problem-solving abilities
• Success in this position requires the ability to make effective, well-informed decisions and recommendations in a highly time-sensitive environment
• Must have a very high level of self-initiative, be able to work independently within a collegial structure, and be comfortable taking risks
• You enjoy asking research questions, expressing opinions, and actively engaging in internal discussions regarding quantitative models and implementation

Join us as an AVP Quantitative Analytics Market Risk Modeler at Barclays Quantitative Analytics Team where you’ll spearhead the evolution of our digital landscape, driving innovation and excellence. You’ll harness cutting-edge technology to revolutionize our digital offerings, ensuring unapparelled customer experiences.

You will be responsible for developing best in class credit risk models using industry leading model development frameworks & methodologies, work in a global quant team, with regulators across the world and cutting-edge technology.

You may be assessed on the key critical skills relevant for success in role, such as experience with end-to-end model development , experience on coding languages like Python OR R OR C++, as well as job-specific skillsets.

To be successful as an AVP Quantitative Analytics Market Risk Modeler you should have experience with:

• You must have knowledge of the following in FRTB, VaR, Expected Shortfall (ES), BASEL, Monte Carlo Simulation, Stress Testing, Exposure Modeling, CVA, Pricing Models, Desk Quants and Strategists, Black-Scholes, Economic Risk Capital, Incremental Risk Charge (IRC), Risk Factor Modelling (Interest Rates, Equities, Credit, Commodities etc.), Back-testing, Numerical Analysis, SR 11/7, SS1/23
• Hands on coding experience (as a full-stack developer / agile developer etc.
• Preferable language is Python, C/C++ etc)
• Hand on experience in Model Development and/or Model Validation (core development experience preferred).

Desired Qualification ;

• Advanced Technical Degree (Master’s / PhD / similar or equivalents) – Statistics, Engineering, Numerical Analysis, Mathematics, Physics, Econometrics, Financial Engineering, Computer Science, Financial Mathematics
• Certification – GARP-FRM, PRM, CQF, AI/ML Courses, Coding and Computer Programming

This role is based out of Mumbai.

Purpose of the role

To design, develop, implement, and support mathematical, statistical, and machine learning models and analytics used in business decision-making

Accountabilities

• Design analytics and modelling solutions to complex business problems using domain expertise.
• Collaboration with technology to specify any dependencies required for analytical solutions, such as data, development environments and tools.
• Development of high performing, comprehensively documented analytics and modelling solutions, demonstrating their efficacy to business users and independent validation teams.
• Implementation of analytics and models in accurate, stable, well-tested software and work with technology to operationalise them.
• Provision of ongoing support for the continued effectiveness of analytics and modelling solutions to users.
• Demonstrate conformance to all Barclays Enterprise Risk Management Policies, particularly Model Risk Policy.
• Ensure all development activities are undertaken within the defined control environment.

Assistant Vice President Expectations

• To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
• Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
• OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
• Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
• Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
• Take ownership for managing risk and strengthening controls in relation to the work done.
• Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
• Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
• Communicate complex information. ‘Complex’ information could include sensitive information or information that is difficult to communicate because of its content or its audience.
• Influence or convince stakeholders to achieve outcomes.

We’re seeking someone to join our team as a Analyst/ Associate to IMM Counterparty Credit Risk, Risk Analytics team

Firm Risk Management

In the Firm Risk Management division, we advise businesses across the Firm on risk mitigation strategies, develop tools to analyze and monitor risks and lead key regulatory initiatives.

Company Profile

Morgan Stanley is an industry leader in financial services, known for mobilizing capital to help governments, corporations, institutions, and individuals around the world achieve their financial goals.

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

What you’ll do in the role:

Primary Responsibilities include, but are not limited to:
• Research, development, enhancement, and documentation of IMM Counterparty credit risk, methodologies, and tools for regulatory and risk management purposes
• Perform analysis including model recalibrations, back-tests, stress tests, scenario, and sensitivity analyses.
• Programming of prototypes/production code (within an established C++/R /Python libraries) which will be productionized.
• Program, test and implement quantitative financial methods using Python, C++, VBA, R, Matlab and SQL
• Utilize advanced statistics, econometrics and mathematical skills including probability theory, stochastic calculus, Monte Carlo simulation, numerical analysis, optimization techniques and time series analysis
• Work with Technology on model testing, implementation, and production
• Collaborate with risk managers and other stakeholders to address their requests and for relevant model enhancements
• Participate in Regulatory and validation exams by providing documentation and responses to regulators and internal validators

What you’ll bring to the role:

Skills Required
• 0-2 (Analyst), 2.5+ (Associate) and 6+ (Director) years of work experience in quantitative modeling, Risk Management, algorithmic trading
• Analytical skills and ability to work with diverse cultures in a global team.
• Strong knowledge of financial traded products e.g. derivatives and their pricing.
• Knowledge and hands-on experience in one of the programming languages R, Python, MATLAB, C# or C++ is strongly preferred.
• Excellent communication skills (Oral and written). Ability to communicate and present logically, precisely and in simple manner, complex and technical issues.

Required Qualifications
• Graduate/Under-graduate/Advance degrees in finance, mathematics, physics econometrics, engineering or other quantitative subjects.
• Candidates should have a strong theoretical foundation in mathematics, quantitative finance and derivatives.
• Candidates will have to deal in Python, SQL queries, and MS-Office on daily basis.

Desirable Skillsets
• FRM, CFA, CQF certification is an advantage.
• Quantitative modeling experience in Finance/ Data Science
• Knowledge of risk mitigation practices and experience with Basel II/III/IV rules will be considered advantageous.
• Experience in one of the following AI, ML, NLP, Big Data Analytics, Tableau is an advantage

The Senior Principal Quantitative Analyst plays a critical role in ensuring the accuracy, integrity, and reliability of quantitative data that powers Morningstars financial models, analytics, and decision-making processes. This role is a cornerstone of the Managed Investment Data (MID) program, which collects, standardizes, and enriches global fund data supporting investors, advisors, and institutions through trusted data and insight. The analyst will lead quantitative data quality design and implementation, develop AI/ML-based validation frameworks, and collaborate with cross-functional teams to strengthen data governance and model readiness. This role reports to the Director, Quality & Transformation within the Managed Investment Data team based in Responsibilities :

• Lead the design, implementation, and enhancement of quantitative data quality frameworks, encompassing statistical validation and anomaly detection.
• Develop AI/ML-driven predictive quality checks, enabling proactive data error prevention and model trustworthiness.
• Apply advanced statistical methodologies linear/non-linear modeling, time series analysis, and Bayesian inference to detect quality drifts and signal inconsistencies.
• Collaborate with quantitative researchers, data scientists, and engineers to ensure data readiness for quantitative models and investment algorithms.
• Create automated, scalable, and auditable data validation pipelines, supporting real-time data monitoring and exception reporting.
• Partner with stakeholders to uphold data governance, privacy, and regulatory compliance standards (MiFID, ESMA, SEC).
• Mentor and guide junior analysts, fostering a culture of excellence, continuous learning, and innovation in quantitative analysis.
• Communicate complex data quality insights and statistical findings in simple terms to senior leadership and non-technical stakeholders.

 

• Drive innovation through automation, reproducible modeling pipelines, and deployment of ML-based data correction systems.
• Contribute to the modernization of Morningstars data architecture by integrating data observability, telemetry, and metadata-driven quality :
• Strong foundation in quantitative finance, econometrics, and applied statistics.
• Deep understanding of financial instruments, fund structures, and performance modeling.
• Proven ability to work with large-scale, structured and unstructured data.
• Excellent analytical, problem-solving, and statistical reasoning skills.
• Strong stakeholder management, communication, and presentation skills.
• Ability to work in a cross-functional, fast-paced environment, and lead through Candidate Profile :
• Masters degree in Data Science, Statistics, Mathematics, Financial Engineering, or Quantitative Finance.
• Professional certifications such as CFA, FRM, CQF, or Six Sigma Black Belt preferred.
• 10+ years of experience in quantitative analytics, model validation, or data quality engineering within financial services, asset management, or fintech.
• Expertise in Python, R, SQL, and familiarity with tools such as MATLAB, SAS, or TensorFlow.
• Experience in AWS ecosystem (S3, RDS, Glue, Athena) and modern data quality platforms.
• Hands-on experience with AI/ML frameworks (scikit-learn, PyTorch, TensorFlow) for anomaly detection and predictive data correction.
• Familiarity with data governance and regulatory standards (GDPR, SEC, ESMA, MiFID).
• Proficiency in Lean, Agile, and automation-first approaches for process improvement.
• Entrepreneurial mindset with a passion for innovation and scalability.
• Strong leadership, mentorship, and collaboration abilities.
• Flexible to adapt to evolving data and technology Competencies :
• Statistical Expertise : Deep proficiency in hypothesis testing, regression modeling, and time-series forecasting.
• AI/ML Integration : Building and deploying predictive quality and anomaly detection models.
• Automation Mindset : Experience with data pipelines, ETL automation, and observability frameworks.
• Data Governance : Comprehensive understanding of metadata management, lineage, and auditability.
• Business Acumen : Translating technical insights into actionable business intelligence.
• Leadership : Guiding teams through analytical rigor, innovation, and continuous is an equal opportunity employer.

The Senior Principal Quantitative Analyst plays a critical role in ensuring the accuracy, integrity, and reliability of quantitative data that powers Morningstars financial models, analytics, and decision-making processes. This role is a cornerstone of the Managed Investment Data (MID) program, which collects, standardizes, and enriches global fund data supporting investors, advisors, and institutions through trusted data and insight. The analyst will lead quantitative data quality design and implementation, develop AI/ML-based validation frameworks, and collaborate with cross-functional teams to strengthen data governance and model readiness. This role reports to the Director, Quality & Transformation within the Managed Investment Data team based in Mumbai.

 

Job Responsibilities :

 

– Lead the design, implementation, and enhancement of quantitative data quality frameworks, encompassing statistical validation and anomaly detection.

– Develop AI/ML-driven predictive quality checks, enabling proactive data error prevention and model trustworthiness.

– Apply advanced statistical methodologies linear/non-linear modeling, time series analysis, and Bayesian inference to detect quality drifts and signal inconsistencies.

– Collaborate with quantitative researchers, data scientists, and engineers to ensure data readiness for quantitative models and investment algorithms.

– Create automated, scalable, and auditable data validation pipelines, supporting real-time data monitoring and exception reporting.

– Partner with stakeholders to uphold data governance, privacy, and regulatory compliance standards (MiFID, ESMA, SEC).

– Mentor and guide junior analysts, fostering a culture of excellence, continuous learning, and innovation in quantitative analysis.

– Communicate complex data quality insights and statistical findings in simple terms to senior leadership and non-technical stakeholders.

– Drive innovation through automation, reproducible modeling pipelines, and deployment of ML-based data correction systems.

– Contribute to the modernization of Morningstars data architecture by integrating data observability, telemetry, and metadata-driven quality measures.

Requirements :

– Strong foundation in quantitative finance, econometrics, and applied statistics.

– Deep understanding of financial instruments, fund structures, and performance modeling.

– Proven ability to work with large-scale, structured and unstructured data.

– Excellent analytical, problem-solving, and statistical reasoning skills.

– Strong stakeholder management, communication, and presentation skills.

– Ability to work in a cross-functional, fast-paced environment, and lead through influence.

Desired Candidate Profile :

– Masters degree in Data Science, Statistics, Mathematics, Financial Engineering, or Quantitative Finance.

– Professional certifications such as CFA, FRM, CQF, or Six Sigma Black Belt preferred.

– 10+ years of experience in quantitative analytics, model validation, or data quality engineering within financial services, asset management, or fintech.

– Expertise in Python, R, SQL, and familiarity with tools such as MATLAB, SAS, or TensorFlow.

– Experience in AWS ecosystem (S3, RDS, Glue, Athena) and modern data quality platforms.

– Hands-on experience with AI/ML frameworks (scikit-learn, PyTorch, TensorFlow) for anomaly detection and predictive data correction.

– Familiarity with data governance and regulatory standards (GDPR, SEC, ESMA, MiFID).

– Proficiency in Lean, Agile, and automation-first approaches for process improvement.

– Entrepreneurial mindset with a passion for innovation and scalability.

– Strong leadership, mentorship, and collaboration abilities.

– Flexible to adapt to evolving data and technology landscapes.

Key Competencies :

– Statistical Expertise : Deep proficiency in hypothesis testing, regression modeling, and time-series forecasting.

– AI/ML Integration : Building and deploying predictive quality and anomaly detection models.

– Automation Mindset : Experience with data pipelines, ETL automation, and observability frameworks.

– Data Governance : Comprehensive understanding of metadata management, lineage, and auditability.

– Business Acumen : Translating technical insights into actionable business intelligence.

– Leadership : Guiding teams through analytical rigor, innovation, and continuous improvement.