Applied Scientist / Applied ML Engineer

The role

The Applied Scientist / Applied ML Engineer will design, build, and deploy machine learning models for pricing, bidding, and decisioning on a cross-border payments platform. This role owns problems end to end from formulation to production.

What you will do

• Own end-to-end ML solutions for pricing, bidding, and risk decisioning.
• Formulate model objectives from first principles and implement them as production-grade services.
• Design and run experiments including A/B tests and offline evaluations.
• Monitor models in production and investigate regressions.
• Continuously improve model performance.
• Partner closely with Product and Backend Engineering.

Required qualifications

• 3-7 years of experience as an ML Engineer, Applied Scientist, or Data Scientist in industry.
• Bachelor's or Master's in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience.
• Strong Python skills, including pandas, NumPy, and scikit-learn.
• At least one of PyTorch, TensorFlow.
• Strong ML fundamentals, including supervised and unsupervised learning, model evaluation, regularization, feature engineering, and statistics.
• Experience designing models from first principles and shipping them to production, in batch or real-time.
• Hands-on experience with data pipelines and ETL, such as Airflow or Spark.
• Strong SQL for feature engineering.
• Experience integrating ML into REST or gRPC APIs and microservice architectures.
• Ability to design and interpret experiments with statistical rigor.
• Strong problem-solving and communication skills.
• Ability to work effectively in cross-functional and distributed teams.

Nice to have

• Optimization, bandits, or decision-making under uncertainty, including dynamic pricing and bid optimization.
• Bidding, auctions, marketplace, or recommendation systems experience.
• Fintech background, including payments, cross-border, lending, trading, or risk and scoring.
• Fraud, AML, credit risk, or vendor risk scoring models.
• Model explainability tooling, including SHAP and feature importance, for auditable decisions.
• Cloud experience (AWS, GCP, or Azure), Docker, and MLOps basics such as model registry and CI/CD.

Benefits

• Real ML in production with direct impact on pricing, risk, and vendor decisions at scale.
• Ownership of core models with room to influence architecture and roadmap.
• Strong engineering peers and complex optimization problems in a high-growth fintech.

About the company

Tolken is an equal opportunity employer committed to creating an inclusive environment for all employees.