2 Numerical Optimization Jobs

Job Title: Lead Quant Analyst, Credit Quants About the Team: DBRS Morningstar Credit Ratings, LLC is registered with the U.S. Securities and Exchange Commission as a nationally recognized statistical rating organization (NRSRO). DBRS Morningstar Credit Ratings issues credit ratings on a variety of security types including corporate and structured finance securities. This Morningstar subsidiary aims to increase market transparency by providing the highest-quality ratings, securities research, monitoring services, operational risk assessments, data, and tools. DBRS Morningstar is a global credit ratings business, formed through the July 2019 acquisition of DBRS by Morningstar, Inc., the ratings business is the fourth-largest provider of credit ratings in the world. DBRS Morningstar is committed to empowering investor success, serving the market through leading-edge technology and raising the bar for the industry. DBRS Morningstar is a market leader in Canada, the U.S. and Europe in multiple asset classes. DBRS Morningstar is driven to bringing more clarity, diversity of opinion, and responsiveness to the ratings process. DBRS Morningstars approach and size provide the agility to respond to customers needs, while being large enough to provide the necessary expertise and resources. The Role: As a Quant Analyst you will execute proprietary research pertaining to building data building various types of credit rating models, such as default models, cashflow models, capital models, regression models covering asset classes of ABS, CMBS, Covered Bond, RMBS, Structured Credit, Corporates, Financial Institutions and Sovereigns. The Credit Ratings Modeling team will collaborate with members from the Credit Ratings, Credit Practices, Independent Review, Data and Technology teams to create class leading models that are as innovative as they are easy to understand in the marketplace. You will be expected to adopt an "iron sharpens iron" attitude where the focus is on making everyone better. The ideal candidate will demonstrate Quant research skills in Credit Modeling alongside Quant Modeling skills such as statistics, Machine Learning, numerical optimization & software engineering skillset within Fintech eco space. This position reports to the Senior Manager of Quantitative Research, Technology. Responsibilities: Support methodology development, Quant Model builds & enhancements for core Quant products as credit predictive models, etc. Participate in building next generation of credit modelling. Maintain and enhance proprietary Python libraries related to model building Leverage structured and unstructured datasets to build new Quant frameworks that would help analysts in informed decision making. Assisting development of Analytics-based solutions, taking ownership of the design and development of solutions to scale information ingestion, storage, computation (training/inference), validation. Participate in analyst conversations for understanding ongoing analyst issues. Requirements: 4 to 5 years of investment research / rating agencies experience with emphasis on fixed income research / analysis, credit modelling. CFA, CQF or postgraduate degree in finance, economics, mathematics, statistics is highly desired. Experience developing Financial Engineering/ Statistical applications on cloud. Experience of statistical models (Regression, Monte Carlo simulations, Numerical Optimization etc.) Experience of developing Quant Models using Python. Experience engineering models on big data. Understanding of both business and technical requirements, and the ability to serve as a conduit between rating team, research and technology Familiarity fixed income. Morningstar is an equal opportunity employer About Us Morningstar DBRS is a leading provider of independent rating services and opinions for corporate and sovereign entities, financial institutions, and project and structured finance instruments globally. Rating more than 4,000 issuers and 60,000 securities, it is one of the top four credit rating agencies in the world. Morningstar DBRS empowers investor success by bringing more transparency and a much-needed diversity of opinion in the credit rating industry. Our approach and size allow us to be nimble enough to respond to customers' needs in their local markets, but large enough to provide the necessary expertise and resources they require. Market innovators choose to work with us because of our agility, tech-forward approach, and exceptional customer service. Morningstar DBRS is the next generation of credit ratings. If you receive and accept an offer from us, we require that personal and any related investments be disclosed confidentiality to our Compliance team (days vary by region). These investments will be reviewed to ensure they meet Code of Ethics requirements. If any conflicts of interest are identified, then you will be required to liquidate those holdings immediately. In addition, dependent on your department and location of work certain employee accounts must be held with an approved broker (for example all, U.S. employee accounts). If this applies and your account(s) are not with an approved broker, you will be required to move your holdings to an approved broker. Morningstars hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. While some positions are available as fully remote, weve found that were at our best when were purposely together on a regular basis, typically three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, youll have tools and resources to engage meaningfully with your global colleagues. R11_DBRSRatingsGmbHIndia DBRS Ratings GmbH, Branch India Legal Entity Show more Show less

SENIOR OPTIMIZATION ENGINEER Hyderabad Role Senior Optimization Engineer Contract type Permanent About the role As a Senior Optimization Engineer you provide technical expertise in the area of numerical optimization of thermo-fluid systems which are core to the Company's business this includes trade-off and optimize energy efficiency, production cost and operating economy. You engage with global product teams to solicit business needs and convert those into computational decision-making workflows, methods and tools to radically impact how company's products are designed, deployed and operated. Key targets include improving engineering effectiveness as well as developing disruptive, innovative methods for model-based design and operation of company's systems. You also take active part in product development to support design engineers in adopting and using new methods and tools. You also work closely with other teams in Systems, Controls, ML/AI COE, including teams responsible for model development (to drive the development of optimization-friendly thermo-fluid models) and controls engineering (to promote the use of computational optimization strategies (MPC, RTO) as needed). We are offering you We are committed to offering competitive benefits programs for all of our employees and enhancing our programs when necessary. A dynamic and international work environment in a company with high technology products. A strong growth strategy and people who are passionate about what they do. Requirements Education MEng or PhD in a relevant Engineering discipline (e.g., applied mathematics, mechanical or chemical engineering) with 5+ years of experience. Skills and qualifications Proven ability to capture engineering design and operation problems as mathematical programming problems (NLPs), including attention to reliable convergence of such problems. Proficient with the mathematical theory (applied mathematics, numerical analysis and functional analysis), algorithmic foundations (notably existence and convergence proofs), and methods/tools for numerical optimization (SQP, interior point method, etc.) of large-scale systems. Experience from using common algorithms/solvers for large-scale gradient-based non-linear programs, e.g., IPOPT, CONOPT, KNITRO, and WORHP, including their respective applicability to different types of problems. Experience from formulating and solving discrete optimization problems and using common algorithms, including CPLEX and Gurobi. Familiarity with physics-based modeling principles and best practices of thermo-fluid systems, such as vapor compression cycles or power plants. Familiarity and experience with development of computational platforms and tools in Python or equivalent. Familiarity with using HPC and cloud-based platforms for computation at scale. Demonstrated ability to work as part of a multidisciplinary team and an entrepreneurial attitude towards technological innovation in a global environment. Self-starter who is well-organized in an international team environment, with proven communication skills. Responsibilities Deployment. Support that methods and tools developed in the group impact the company's business through engagement in global product projects, including capture and formulation of computational problems arising in such projects. Methods, tools and algorithms. Ensure that appropriate computational methods, tools and algorithms for large-scale numerical optimization are based on sound mathematical foundations and are deployed to match the needs of the company's business, including contributing to the architecture, development, testing and documentation of the methods and tools developed in the group. Modeling for optimization. Support development of mathematical models for thermo-fluid systems are built based on principles and best practices that secure reliable application of numerical optimization algorithms. Talent. Support development and training of staff within company's product teams and within the Computational Engineering group; contribute to talent pipeline by supervising student internships and theses.