Machine Learning Engineer

About ArborKnot:

ArborKnot is a dynamic global financial group specializing in pricing & managing distressed consumer receivables in an amicable way. With $765 million AUM (as at end May 25'), our mission is to alleviate the burden of debt on individuals, enabling them to lead better lives through innovative & data driven recovery practices.

We're challenging traditional market practices. Our innovative approach fosters a dynamic marketplace for our clients and consumers.

With offices in Sydney, Berlin, New York, and an innovation lab in Tel Aviv, Israel, ArborKnot offers a unique opportunity to be part of a fast-growing, multicultural team dedicated to driving positive industry and social change.

JOB SUMMARY

As a
Machine Learning engineer

at ArborKnot, you will be at the forefront of transforming the debt recovery landscape through data. You'll turn complex behavioral signals into
scalable, reliable systems

that drive real-world financial decisions, working with highly
imbalanced datasets

to predict repayment likelihood, detect anomalies, and optimize recovery strategies. 

Your day-to-day will involve more than just model deployment - you'll help shape our model lifecycle end-to-end, from experimentation and explainability through pipelines and monitoring. You'll get the chance to dig deep into our pricing and recovery strategy , surface the most meaningful drivers of payments behavior, and contribute to research and feature engineering. You'll take ownership of building systems that allow us to scale with confidence, making our pricing, recovery, and risk strategies both cutting-edge and production-ready.

KEY RESPONSIBILITIES 

• Build and maintain scalable ML infrastructure, CI/CD  for model development, deployment  and life cycle management
• Own ML
  Experiment tracking,  and finetuning and reproducibility.
• Design evaluation and training  pipelines and infrastructure to improve the systems we build.
• Develop, validate, optimize, and deploy ML  models aligned with business goals, ensuring explainability, robustness and post-implementation validation.
• Maintain and develop model lifecycle - feature engineering, research, implementation, model explainability, and continuous monitoring

QUALIFICATIONS

• Bachelor's degree (BSc) in Statistics, Computer Science,  or other relevant fields.
• M.Sc. in Statistics or Computer Science - advantage
• 3+ years of experience as a Data Scientist ,Machine Learning Engineer
• Proven experience developing, evaluating, and deploying machine learning models in production environments.
• Proven experience in ML operations toolkit( e.g. ML flow, weights and Weights & Biases )
• Strong programming skills in
  Python

(pandas, NumPy, scikit-learn)
- Experience with SQL and writing SQL queries

Advantage 

• Practical experience handling
  imbalanced datasets

and rare event prediction (e.g. fraud, churn, default) - big advantage.
- Experience working in
FinTech

,
collections

, or
fraud/risk modeling

domains
- Experience with
Data pipelines

e.g. Airflow
- Experience with
cloud platforms

(Google Cloud Platform, AWS, or Azure-
Advantage

)
- Experience with
Bigquery

Soft Skills

• Strong problem-solving skills with a proactive approach mindset
• Fluent in English

(written & spoken).
- Exhibits a high level of responsibility and ownership in all aspects of work.

At ArborKnot, you will be at the forefront of financial innovation, working in a true multicultural environment that values creativity, collaboration, and growth. We value diversity and strive to create an inclusive work environment where employees can bring their whole self to the workplace.

If you are a highly motivated and analytical individual with a passion for data and finance, we would love to hear from you. Join us to shape the future and develop new markets.