At Cardo AI, we are on a mission to build the best technology empowered by AI algorithms for private market investments.

Our engineers don’t just make things – we make things possible. We provide infrastructure, speed and precision to institutional investors, banks and credit originators in solving the most challenging and complex credit investment problems.

We are looking for a motivated Financial Engineer to join our Data Science Team and support Cardo AI’s rapid growth. Typically, you have a deep quantitative understanding of financial markets and experience in data modeling. You also have either experience or a strong interest in Machine Learning and Deep Learning applied to Finance. You will advance the company’s Financial Engineering capabilities, be it by pairing your financial background with the data science team’s ML modeling or by suggesting classical baselines to machine learning models.

• Background in econometrics, quant finance, math, physics, engineering, computer science or equivalent
• 4+ years of experience as a Financial Engineer and/or Quantitative Finance Analyst.
• Experience in at least one of the following: credit risk modeling, credit pricing modeling, private credit quantitative modeling
• Fluent in the analysis of financial data
• Experience in Python programming
• Experience with git for version control
• Familiar with SQL or PySpark
• Experience working in a team
• Ability to quickly learn complex research findings and translate them into production grade solutions
• Completely aware of the fact that higher complexity has to be well motivated by higher performance

• PhD in hard science –a relevant degree will count toward work experience–
• International work experience
• Experience in Private Debt Markets
• Experience with any of the following: R, Java, Scala, C++

• Implement classical quantitative finance and machine learning models, specifically focusing on Private Debt Markets.
• Use your skills and quantitative finance knowledge to support the Data Science Team through analyzing data and suggesting classical baseline models.
• Educate the rest of the data science team on financial engineering best practices and financial markets in general.
• Watch your ideas make an impact. Your knowledge and expertise will inform machine learning modeling across some of the most ambitious projects we are working on.
• Collaborate with an amazing team. You will collaborate closely with other teams at Cardo AI to create great products. This includes working closely with the product teams to understand the customer story, design team to understand the motivation behind the UI/UX, and the data engineering team to understand our data structures and frameworks, and software engineering team to orchestrate new data needs and deploy models in production.
• Be part of a growing fintech company. Cardo AI has won the first place at European Big Data Value Forum, was selected among the top 20 scale-ups in Europe by the EIT Digital (leading European digital innovation and entrepreneurial programs), and named top 15 European Startups at Money20/20 (Amsterdam) and top 8 Fintech startups at Lendit (London).

• An opportunity to develop models that matter and tackle real challenges of private market investments. Every day, our platform is making a difference for our clients, supporting them to do their job better and empowering everyone to make better decisions. We are improving the way private markets operate.
• Agile development process: Some say they do it, we truly believe in doing it properly and in whatever form that allows the team to work at its best! With a wholesome approach to applying the Scrum framework, clear roles and responsibilities, we make sure to give every one of our co-creators a reputable voice on the user stories under their supervision. We don’t skip inclusive planning and are very thorough on our Retrospectives, enabling action to improve the overall experience sprint after sprint, one step at a time.
• A good work life balance. We know that solving big problems can keep you up for long hours, but this is not the way to go. We have assigned a work-life balance officer that is responsible for our engineers in finding solutions and avoiding any situation of potential stress and burnout. After all, why do we even bother with Scrum time boxes?
• Flexible and Remote Work. Remote opportunities, work from home days, and flexible vacation policies allow everyone to optimize their work-life balance. We are a result oriented business – face-time not required, although you might just want to spend your entire day with your great team. You choose when and where you want to work!
• Learning and Development budget for courses, workshops, conferences.
• An Entrepreneur friendly environment. You can participate in “Intrapreneurship in Cardo AI”, a project where ideas come to life from team members or “Cardo AI Startup Incubator” where you can invest alongside Cardo AI and become an entrepreneur.
• Team Outings & Events. We bond outside of work over games, happy hours, concerts, chess, play-station, karaoke, volleyball, yoga, and more. We love to organize team retreats on the beach and on the mountains.
• Become part of Cardo AI: We offer a stock option plan to all our key people so that you can become an integral part of the company and benefit from a potential exit event.

Getting around Cardo AI (1st week)

1. Hiring and initial orientation, introduction to the domain and main products.
2. Meet your designated mentor.
3. Meet the team in Milan and learn about the structure of the company.
4. Meet the team in Tirana and London
5. Learn the main internal processes of the company: Cardo AI values (Excellence, Context over control, Care, and Self Improvement); Our work principles; Mission and Vision; Career path progression
6. With your mentor, organize your first “Way of working feedback” – session in which both you and your mentor share the pros and cons of you working style, characteristics and ways to cope with them
7. Start getting used to our agile research and development framework and feedback loop

Start getting used to your responsibilities (1st Month)

1. Learn about the current projects of the Data Science Team
2. Understand in detail the current business needs and modeling requirements
3. Keep abreast of the latest Financial Engineering methodologies that could be applied to our challenges
4. Catch up with the Data Science Team’s R&D topics through our internal knowledge base
5. MLOps Onboarding on the team’s low code deployment framework

30 Days check-in questionnaire

1. Check in with our Talent Manager about the onboarding process: How did you feel? Were your expectations met? Pros and Cons! What could have been done differently and better?
2. Conduct a one-to-one feedback session with your mentor

Delivering on your skills (Month 2-3)

1. Collaborate in the implementation of our credit risk and portfolio optimization models.
2. Proactively contribute to the teamwork and involve yourself in working in smart code and clean standards
3. Meet your supervisor and designated advisor to provide and receive feedback on your first three months

Probation period performance review (Month 3)

1. Meet your mentor and perform a thorough assessment of main actions carried out in the first 3 months
2. Receive and provide feedback on the deliveries and what could be improved further
3. Discuss with your mentor your training package and specific conferences that you would like to participate in

First formal performance review (Month 6)

1. Organize and go through your first formal performance review
2. Assess the objectives set in the first 6 months
3. Feedback on the deliveries and what could be improved
4. Feedback on impediments and how to cope with them
5. Propose new objectives for the next 6 months.