Location: India

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.

TAS is seeking talented professionals to join our fast-growing Digital Value Creation group (DVC) at the Associate level. DVC provides our clients value-creating insights from vast market, operational, and financial data. DVC professionals work closely with HL due diligence, valuation and investment banking teams alongside clients’ deal and operating teams. As a professional in the group, you will be teamed with highly talented and dedicated M&A professionals in various industry groups including Industrials, Consumer, Technology, Business Services and Financial Services. This opportunity provides you broad exposure to different transactional issues affecting businesses in an M&A environment.

This is a unique opportunity for someone with proficiency in data analytics along with experience in applying data analytics techniques to financial and operational analyses that is fundamental to an M&A process. DVC provides you ample exposure to the M&A and corporate finance industry and capital markets. You will further develop and extend your data analytics knowledge, and hone your interpersonal skills as you deliver valuable insights that derive transaction and strategic decision making for internal and external stakeholders.

Responsibilities
• Participate in buy-side and sell-side M&A engagements and data-focused operational reporting engagements
• Lead engagement tasks or workstreams within an engagement, taking ownership of the execution, and quality and timeliness of deliverable to clients
• Communicate effectively with internal project teams as well as client teams, showing ability to put forth points of view and drive discussions towards required objectives
• Gather, evaluate, sanitize, and organize applicable meta data
• Prepare data workflows to clean and combine data from multiple sources
• Prepare data visualizations and dashboards to deliver key insights
• Generate insights on the drivers of business growth, profitability, and liquidity
• Identify the key business risks and opportunities impacting business valuation
• Be willing to learn and train peers in the advisory practice on data analysis and visualization tools
• Continuously develop industry knowledge and qualifications
• Be able to work on multiple assignments simultaneously
• Support and actively participate in business development efforts
• Review the work of team members to ensure desired quality and insights

Basic (must-have) Qualifications
• Bachelor’s degree in technology / computer science / accounting / finance or quantitative finance, or similar (with concentration in data analytics or another quantitative field)
• Experience in financial analytics based on sound understanding of financial statements like Profit & Loss and Balance sheet and ability to analyze financial and operating performance of a company
• Hands-on experience in working on one of the data wrangling / ETL tool i.e. Alteryx, Dataiku etc.
• Experience in, and sound knowledge of data visualization tools, either Tableau or Power BI
• Strong command of Microsoft Excel formulas, PowerPivot, Power Query, etc.
• Experience working in a global organization across different time zones, managing both internal and external stakeholders
• Exceptional work ethic, high motivation, and a demonstrated ability and desire to work cooperatively with team members and client professionals
• Strong analytical abilities
• Conduct technical training and best practice sessions for team members
• Exceptional verbal and written communication skills

Preferred (good-to-have) Qualifications
• Post graduate degree or diploma, or certification in any of the above fields of study or business administration (for instance MBA, CFA, CQF etc.)
• Experience in M&A and financial consulting areas such as Financial due diligence, Valuation, Financial Planning & Analysis will be a strong advantage
• Strong command of at least one programming language Python, R, VBA
• Prior work experience in relational database management systems (including experience in SQL Server, Snowflake, or similar)
Work experience
• 3 (three) to 7 (seven) years of professional experience

Deloitte Model Risk Management (MRM) is one of the services we offer to our clients where we help them manage their risks around model usage. The team is comprised of professionals with diverse backgrounds, including Masters in Statistics, Mathematics, Physics, Finance, Financial Engineering and PhDs in various quantitative fields, etc. Our team is focused on qualitative assessment and quantitative modeling in the areas of Market Risk, Credit Risk, Operational Risk, Liquidity Risk, Fraud Risk as per regulatory guidelines like CCAR/Stress Testing, BASEL II.5 / III in US and CRD IV/CRR in EMEA regulations. The team also does valuation of complex financial products such as derivatives and structured products. Our value proposition includes industry, financial accounting and business process knowledge, proven methodologies that include risk and control concepts, deep expertise in advanced quantitative, data extraction, data mining and analytical skills.

The key job responsibilities will be to:

• Work on consulting projects related to financial instrument modeling, model review, securities pricing, and risk management including support for regulatory compliance.
• Develop or validate equity, FX, and hybrid based exotic pricing models with a focus on conceptual assessment and assumptions testing.
• Perform quantitative analysis focused on Profit Attribution Analysis (PAA), Stress Testing and Non- modellable Risk Factors (NMRFs) for the models being validated.
• Assess valuation methodology for fixed income instruments and derivatives on interest rates, foreign exchange, equity, and credit.
• Design, implement and critique on calibrations of parametrized valuation models such as Black Scholes, Hull & White, SABR, Heston, etc.
• Assess IPV methodology for external clients covering products across all asset classes.
• Assist client in Advisory projects around the evolving issues e.g., XVA, IBOR Transition, etc. that affect the valuation of derivatives and structured products.

Qualifications

Must Have Skills/Project Experience/Certifications:

• 1-4 years of experience with quantitative analysis technical tools such as Python, R, MATLAB, SAS, etc.
• Familiarity with valuation of fixed income instruments, derivatives on interest rates, foreign exchange, equity, and credit. Understanding of financial derivatives, stochastic calculus, and numerical techniques for derivatives pricing (Monte Carlo / Finite Difference).
• Familiarity with various quantitative measures related to Market Risk (e.g., VaR, Expected Shortfall, etc.) and Counterparty Credit Risk (e.g., Expected Exposure, Expected Potential Exposure, etc.)
• Familiarity with tools like Bloomberg, Refinitiv, Murex, etc. is a plus.
• Experience in model validation like Asset Liability Management, Economic Capital Models, etc. is a plus.
• Understanding of financial regulations (like FRTB), products or financial processes is a plus
• Ability to explain difficult financial modeling/valuation concepts to diverse audiences and to experts at various clients.
• Related bank/consulting experience is a plus.

Good to Have Skills/Project Experience/Certifications:

• Certifications: CFA / FRM / CQF
• Experience in programming languages such as Python or R

We are looking for high-caliber professionals with strong foundations in credit risk modeling and hands-on experience in AI/ML techniques. The ideal candidate will contribute to the development and validation of regulatory and strategic risk models, while also applying machine learning and generative AI techniques to enhance model accuracy, efficiency, and interpretability.

Key Responsibilities

• Develop, validate, and document credit risk models (PD, LGD, EAD) for retail and wholesale portfolios across regulatory (CECL, IFRS 9, Basel) and business-use contexts.
• Apply AI/ML algorithms (e.g., LightGBM, XGBoost, Random Forest, Neural Networks) to improve prediction power and model performance beyond traditional approaches.
• Implement Generative AI and LLM-based applications using RAG pipelines, document intelligence, and model documentation automation. Experience with agentic frameworks like Autogen, LangChain, or similar would be helpful.
• Experience of development and deployment of models in cloud-based platforms such as Azure, AWS, GCP etc.
• Design explainable AI solutions by incorporating techniques like SHAP, LIME, and feature attribution methods to enhance transparency in high-stakes modeling environments.
• Partner with cross-functional teams, including business stakeholders, technology teams, and model governance, to ensure model alignment with business objectives and regulatory expectations.
• Contribute to innovation initiatives and support proposal development, thought leadership, and solution architecture in the AI/ML space.

Required Skills & Experience

• 2–6 years of total experience, with minimum 2 years in AI/ML or GenAI model development or validation.
• Strong understanding of credit risk modeling frameworks, scorecard development, and risk metrics (e.g., RWA, Expected Loss, Economic Capital).
• Proficient in Python and SQL, with hands-on experience using ML libraries such as scikit-learn, Tensorflow, Pytorch and transformer-based LLM packages
• Familiarity with regulatory standards such as CECL, IFRS 9, CCAR/DFAST, Basel II/III, SR 11-7, and model governance best practices.
• Exposure to cloud environments (Azure preferred), version control (Git), and workflow automation tools.
• Experience with credit bureau data, vendor models (e.g., FICO, Moody’s, S&P), and financial benchmarking is a plus.
• Ability to clearly communicate complex technical content to non-technical stakeholders through reports, dashboards, and presentations.

Education & Certifications:

• Master’s degree or higher in Statistics, Mathematics, Economics, Data Science, Engineering, or Finance.
• Professional certifications such as FRM, CFA, CQF, or in product management equivalent are preferred.
• Contributions to opensource AI / ML projects and competitions is preferred

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

The key job responsibilities will be to:

• Work on consulting projects related to financial instrument modeling, model review, securities pricing, and risk management including support for regulatory compliance.
• Develop or validate equity, FX, and hybrid based exotic pricing models with a focus on conceptual assessment and assumptions testing.
• Perform quantitative analysis focused on Profit Attribution Analysis (PAA), Stress Testing and Non- modellable Risk Factors (NMRFs) for the models being validated.
• Assess valuation methodology for fixed income instruments and derivatives on interest rates, foreign exchange, equity, and credit.
• Design, implement and critique on calibrations of parametrized valuation models such as Black Scholes, Hull & White, SABR, Heston, etc.
• Assess IPV methodology for external clients covering products across all asset classes.
• Assist client in Advisory projects around the evolving issues e.g., XVA, IBOR Transition, etc. that affect the valuation of derivatives and structured products.

Qualifications

Must Have Skills/Project Experience/Certifications:

• 1-4 years of experience with quantitative analysis technical tools such as Python, R, MATLAB, SAS, etc.
• Familiarity with valuation of fixed income instruments, derivatives on interest rates, foreign exchange, equity, and credit. Understanding of financial derivatives, stochastic calculus, and numerical techniques for derivatives pricing (Monte Carlo / Finite Difference).
• Familiarity with various quantitative measures related to Market Risk (e.g., VaR, Expected Shortfall, etc.) and Counterparty Credit Risk (e.g., Expected Exposure, Expected Potential Exposure, etc.)
• Familiarity with tools like Bloomberg, Refinitiv, Murex, etc. is a plus.
• Experience in model validation like Asset Liability Management, Economic Capital Models, etc. is a plus.
• Understanding of financial regulations (like FRTB), products or financial processes is a plus
• Ability to explain difficult financial modeling/valuation concepts to diverse audiences and to experts at various clients.
• Related bank/consulting experience is a plus.

Please see job 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

Skills Required

What you’ll bring to the role:

• 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

As a young and quickly growing company that combines financial understanding and engineering, MerQube’s environment is fast-paced, collegial, flexible and offers meaningful work. We are hiring an Index Operations Analyst to be responsible for the daily maintenance of index operations, focusing on data quality and supporting both internal and external ad hoc projects.