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Join The Fintech

Revolution

Help us shape the fintech world and accelerate digital transformation

Solve The Most Complex

Challenges In The Market

Every day, our team builds models and solutions that tackle real problems inside the structured finance industry, making a difference in the market and empowering our clients to make better, faster and informed investment decisions.

We are

Driven by

Excellence

At Cardo AI, we strive for excellence in each step of our journey. We start by hiring the best talent that will help the company reach its goals. We encourage creative thinking, empowering everyone for driving decisions that will contribute to our success. Monthly 1-2-1 sessions ensure continuous feedback to reach goals, and we recognize talent’s achievements through a performance based process with salary increases, bonuses, training packages, welfare benefits, etc. 

We Value

Context Over

Control

We promote working habits that foster everyone to make the best possible decisions. We define clear roles, strategy and we set concrete goals with measurable outcomes. We fundamentally believe in transparency and trust, avoiding top down decision making and incentivizing each individual to take the lead on their activities and tasks. 

We encourage

Self-Improvement

We believe in continuous improvement and we foster a growth mindset within Cardo AI. We challenge our people to reach their full potential. Our career planning and learning programs ensure that everyone has the right tools, mentorship and guidance to grow inside the organization. 

We care for

Each Other

We are stronger together. When we act as a unified force, nothing can stop us. That is why we believe that our colleagues are our greatest source of support. At Cardo AI we promote a healthy environment where your peers’ success is your success. Our buddy and mentorship programs, alongside recurring team gatherings and activities enable everyone to be heard, respected and appreciated for their unique qualities and strengths. 

Customer

Centricity

We are obsessed with customer satisfaction. Our goal is to provide a delightful experience to everyone that interacts with our Products, Services and People. We are eager to seek insights from our customers on how they perceive, interact and use what we offer. We shape our actions to build a trustful and loyal relationship where our customers advocate for us. 

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At Cardo AI, we don't just open doors to talent; we ensure they open equally for all

Discover a workplace where your skills and diversity are celebrated. Read our Gender Equality Plan here to learn how we champion inclusivity.

Sounds Cool?

There's Even More!

Flexibility unleashed

Embrace remote work and enjoy flexible hours that suit your lifestyle.

Bring brilliance

Be rewarded generously for referring talented friends to our team.

Inspiring rewards

Competitive salary, performance-based bonuses every 6 months, and quarterly bonuses for top performers.

Claim your stake

Participate in our exclusive stock option plan and
share in our success.

Work Hard, Play Harder

Enjoy regular social events and amazing company retreats each year.

Accelerate your career

Witness firsthand the power
of growth with abundant learning opportunities
and a dedicated training budget.

Boost Your Career

Wondering What Life

At Cardo AI Is Like?

Job Openings

Find your next career opportunity in Cardo AI! Can’t find the role you’re looking for? Submit an open application, we would love to meet you!
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We are looking for a highly motivated and creative Ph.D. candidate interested in topics at the intersection of machine learning for macroeconomic forecasting and causal inference. This Ph.D. project aims to benchmark various forecasting methods and then implement a robust tool for causal modeling drawing from recent advances in both fields. During the project, you will closely collaborate with industry and a doctoral training network spread throughout Europe, including extended research stays abroad. 

The successful applicant will join the Data Science Team within Cardo AI, with the degree conferred in collaboration with the WU Vienna University of Economics and Business.

Background 

This Ph.D. position is one of 2 positions at Cardo AI in the context of the international Marie Skłodowska-Curie Actions project DIGITAL. For the general description of DIGITAL and the Ph.D. positions, please check the official project webpage.  

DIGITAL’s main goal?

To significantly advance the methodologies and business models for Digital Finance through the use of five interconnected research objectives:

  1. Ensure sufficient data quality to contribute to the EU’s efforts of building a single digital market for data 
  2. Address deployment issues of complex artificial intelligence models for real-world financial problems 
  3. Validate the utility of state-of-the-art explainable artificial intelligence (XAI) algorithms to financial applications and extend existing frameworks 
  4. Design risk management tools concerning the applications of the Blockchain technology in Finance 
  5. Simulate financial markets and evaluate products with a sustainability component 

The outcomes from this individual research project will contribute to the expanding body of knowledge concerning the applications of cutting-edge machine learning and artificial intelligence techniques to traditional financial problems. Recent findings from the ML literature on time series forecasting will be applied in the first phase of the project. In the second phase of the project, the successful candidate will be able to conduct research in the field of causal inference in finance, which also is a very promising field of research.

The challenge 

Macroeconomic factors such as central bank interest rates, inflation, unemployment rates, and house price indices play a crucial role in financial markets, presenting both challenges and opportunities for forecasting. This project is designed to tackle the complexity of predicting these macroeconomic indicators’ future values by benchmarking a variety of methodologies spanning from classical statistical learning to modern machine learning techniques. The initial phase of the project will focus on leveraging recent advancements in machine learning literature to accurately predict these indicators, setting the stage for more informed financial analysis and decision-making.

The longer term objectives of this project extend beyond mere prediction, exploring future market scenarios in a counterfactual fashion and, more generally, delving into causal inference in  econometrics.

Your profile 

We look for a highly motivated, enthusiastic researcher who is driven by curiosity and has/is: 

General skills: 

  • Master’s degree in STEM, Economics, Finance or related fields
  • A strong passion and outstanding skills in data science and experience working with programming languages and statistical software such as Python, C++, C# or R; 
  • Knowledge on quantitative modeling of financial markets, econometric techniques, machine learning or quantitative empirical research methods; 
  • The ability to work on real-world problems in an interdisciplinary and internationally oriented environment; 
  • Good communication skills and an excellent command of English. 

Project-specific skills:

  • Experience with the main time series forecasting and causal inference techniques
  • Good Python programming skills. Experience with machine learning libraries (e.g., numpy, pandas, statsmodels, PyTorch) is helpful. 
  • Experience with git for version control and SQL for data manipulation
  • Experience in data analysis and data visualization
  • Experience with classical Statistical/Machine Learning techniques like linear models, tree based models and the main neural network architectures

Interested and motivated candidates are encouraged to apply, even when not yet possessing all desired skills. Through self-driven learning and doctoral training, you will be able to develop relevant skills on the job.  

Our offer 

Benefits offered as part of this position include: 

  1. Living allowance of EUR 2171/person month (net), Mobility allowance of EUR 600/person month, Family allowance of EUR 660/person month. More details on the allowances can be found here.
  2. Company-provided laptop, monitor, keyboard and mouse, flexible working time, work from home or office, internal and external education as per the set OKR/KPIs guided by mentors, meal vouchers of EUR 5 for each working day.
  3. This PhD position includes two research stays at industrial partners. The first stay will be at the Fraunhofer Institute for Industrial Mathematics in Kaiserslautern (Germany), under the supervision of Prof. Dr. Ralf Korn. It is scheduled to start on Month 18 and last for 4 months, during which exposure to a world-leading research centre, infrastructure and applied industry-research will be provided.  The second stay will be carried out at WU Vienna University of Economics and Business in Vienna (Austria), under the supervision of Prof. Dr. Kurt Hornik and Dr. Ronald Hochreiter. It is scheduled to start on Month 24 and last for 18 months, covering theoretical modeling and mathematics for deep learning.
    1. Any potential change from the initial plan regarding research stays will be dully notified to candidates and reflected in the job advert description.

How to apply 

Are you interested to be part of our team? Please submit your application, and include:  

  • A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, and motivations to apply for this position;  
  • A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references;  
  • An IELTS-test, Internet TOEFL test (TOEFL-iBT), or a Cambridge CAE-C (CPE). Applicants who have not had secondary and tertiary education in English can only be admitted with an IELTS-test showing a total band score of at least 6.5, internet. TOEFL test (TOEFL-iBT) showing a score of at least 90, or a Cambridge CAE-C (CPE).  

Please ensure that your application is submitted by the deadline and note that we will start conducting interviews with short-listed candidates starting from April 2024; however, the application deadline is the 5th of July 2024. Do not make submissions via email as they will not be considered.  

Additional information can be acquired via email from Dr. Gennaro Di Brino (gennaro.dibrino@cardoai.com) with a cc to Dr. Jorg R. Osterrieder (jorg.osterrieder@utwente.nl). 

Diversity and Inclusion

We encourage applications from minorities and underrepresented groups to enrich our diverse academic community. Candidates will be selected on the basis of their competence and ability, and all applicants will be given equal opportunities. We acknowledge the importance of diversity and inclusion for innovation and excellence in digital finance research.

About the department 

The Cardo AI Data Science Team includes people with world-class backgrounds in hard science and engineering. The team is structured around our main areas of activity, and it consists of three units: Pricing and Optimization, Tabular Data Modeling, and Unstructured Data Modeling. We work on a diverse range of problems, from Portfolio Optimization (loans, notes, etc.) to Probabilistic Classification (e.g., probability of default), from Time Series Forecasting to Language Modelling. Our mission is to assist decision makers in structured finance markets with the latest, most reliable modeling techniques available in machine learning and quantitative finance research.

About the organization 

Cardo AI is a Milan-based software, data, and intelligence company innovating the asset based finance and private credit market. Founded in 2018, our solutions have been designed to help investors, issuers, servicers and lenders unlock opportunity from complexity and transact faster, better, and with lower costs. As of January 2024, our platform manages over $24bn in complex credit assets, standardizing over +35bn data points coming from 130+ systems all over the world.

Ready to Redefine Work? Join Cardo AI and let boldness and creativity reign. Together, we’ll shape the future of fintech! Apply now.



We are looking for a highly motivated and creative Ph.D. candidate interested in long term hierarchical and grouped time series forecasting. This Ph.D. project aims to predict cash flows of a private/illiquid portfolio (e.g., Mortgages, SME loans, consumer loans) across multiple geographies and industry sectors. During the project, you will closely collaborate with industry and a doctoral training network spread throughout Europe, including extended research stays abroad. 

The successful applicant will join the Data Science Team within Cardo AI, with the degree conferred in collaboration with the University of Kaiserslautern-Landau (RPTU)

Background 

This Ph.D. position is one of 2 positions at Cardo AI in the context of the international Marie Skłodowska-Curie Actions project DIGITAL. For the general description of DIGITAL and the Ph.D. positions, please check the official project webpage.  

DIGITAL’s main goal? 

To significantly advance the methodologies and business models for Digital Finance through the use of five interconnected research objectives:

  1. Ensure sufficient data quality to contribute to the EU’s efforts of building a single digital market for data 
  2. Address deployment issues of complex artificial intelligence models for real-world financial problems 
  3. Validate the utility of state-of-the-art explainable artificial intelligence (XAI) algorithms to financial applications and extend existing frameworks 
  4. Design risk management tools concerning the applications of the Blockchain technology in Finance 
  5. Simulate financial markets and evaluate products with a sustainability component 

The outcome of this individual research project will contribute to the expanding body of knowledge concerning the applications of cutting-edge machine learning and artificial intelligence techniques to traditional financial problems. Specifically, the first phase of the project will concentrate on missing value imputation for loan payment time series, while the second phase will adopt a more general predictive approach, that of grouped time series forecasting, possibly incorporating the first step.

 The challenge 

Data incompleteness and inconsistency in institutional loan portfolios significantly hampers the accuracy and effectiveness of financial models. Candidates will tackle issues arising from missing information within loan datasets, e.g., repayment and loan status, employing a variety of advanced missing value imputation techniques.

The primary research objective is to develop a sophisticated machine learning tool capable of grouped time series forecasting for private debt portfolios that span diverse geographies and sectors. By leveraging both public and proprietary data, the successful candidate will work on refining and advancing financial modeling techniques. This initiative aims to provide institutional investors with more accurate and useful insights to refine investment strategies and enhance model robustness, thereby making significant contributions to financial analytics and effective investment management.

Your profile 

We look for a highly motivated, enthusiastic researcher who is driven by curiosity and has/is: 

General skills: 

  • Master’s degree in STEM, Economics, Finance or related fields
  • A strong passion and outstanding skills in data science and experience working with programming languages and statistical software such as Python, C++, C# or R; 
  • Knowledge of quantitative modeling of financial markets, econometric techniques, machine learning or quantitative empirical research methods; 
  • The ability to work on real-world problems in an interdisciplinary and internationally oriented environment; 
  • Good communication skills and an excellent command of English. 

Project-specific skills: 

  • Experience with the main time series forecasting and missing value imputation techniques
  • Good Python programming skills. Experience with machine learning libraries (e.g., numpy, pandas, statsmodels, PyTorch) is helpful. 
  • Experience with git for version control and SQL for data manipulation
  • Experience in data analysis and data visualization
  • Experience with classical Statistical/Machine Learning techniques like linear models, tree based models and the main neural network architectures

Interested and motivated candidates are encouraged to apply, even when not yet possessing all desired skills. Through self-driven learning and doctoral training, you will be able to develop relevant skills on the job.  

Our offer 

Benefits offered as part of this position include: 

  1. Living allowance of EUR 2171/person month (net), Mobility allowance of EUR 600/person month, Family allowance of EUR 660/person month. More details on the allowances can be found here.
  2. Company-provided laptop, monitor, keyboard and mouse, flexible working time, work from home or office, internal and external education as per the set OKR/KPIs guided by mentors, meal vouchers of EUR 5 for each working day.
  3. This PhD position includes two research stays at industrial partners. The first research stay will be carried out at Humboldt University of Berlin (Germany), under the supervision of Prof. Dr. Wolfgang Härdle. It is scheduled to start on Month 12 and last for 18 months, during which the candidate will be exposed to various bodies of research on Fintech innovations and their applications. The second research stay will be carried out at Athena Research Centre in Athens (Greece), under the supervision of Prof. Dr. Ioannis Emiris. It is scheduled to start on Month 33 and last for 4 months, during which the candidate will be exposed to applied industry-research in a world-leading research center and make use of its infrastructure.
    1. Any potential change from the initial plan regarding research stays will be dully notified to candidates and reflected in the job advert description.

How to apply 

Are you interested to be part of our team? Please submit your application, and include:  

  • A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, and motivations to apply for this position;  
  • A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references;  
  • An IELTS-test, Internet TOEFL test (TOEFL-iBT), or a Cambridge CAE-C (CPE). Applicants who have not had secondary and tertiary education in English can only be admitted with an IELTS-test showing a total band score of at least 6.5, internet. TOEFL test (TOEFL-iBT) showing a score of at least 90, or a Cambridge CAE-C (CPE).  

Please ensure that your application is submitted by the deadline and note that we will start conducting interviews with short-listed candidates starting from April 2024; however, the application deadline is the 5th of July 2024. Do not make submissions via email as they will not be considered.  

Additional information can be acquired via email from Dr. Gennaro Di Brino (gennaro.dibrino@cardoai.com) with a cc to Dr. Jorg R. Osterrieder (jorg.osterrieder@utwente.nl). 

Diversity and Inclusion

We encourage applications from minorities and underrepresented groups to enrich our diverse academic community. Candidates will be selected on the basis of their competence and ability, and all applicants will be given equal opportunities. We acknowledge the importance of diversity and inclusion for innovation and excellence in digital finance research.

About the department 

The Cardo AI Data Science Team includes people with world-class backgrounds in hard science and engineering. The team is structured around our main areas of activity, and it consists of three units: Pricing and Optimization, Tabular Data Modeling, and Unstructured Data Modeling. We work on a diverse range of problems, from Portfolio Optimization (loans, notes, etc.) to Probabilistic Classification (e.g., probability of default), from Time Series Forecasting to Language Modelling. Our mission is to assist decision makers in structured finance markets with the latest, most reliable modeling techniques available in machine learning and quantitative finance research.

About the organization 

Cardo AI is a Milan-based software, data, and intelligence company innovating the asset based finance and private credit market. Founded in 2018, our solutions have been designed to help investors, issuers, servicers and lenders unlock opportunity from complexity and transact faster, better, and with lower costs. As of January 2024, our platform manages over $24bn in complex credit assets, standardizing over +35bn data points coming from 130+ systems all over the world.

Ready to Redefine Work? Join Cardo AI and let boldness and creativity reign. Together, we’ll shape the future of fintech! Apply now.



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