Applied Scientist / Applied ML Engineer
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About this role
The Role
We are looking for an Applied Scientist / Applied ML Engineer to design, build, and deploy machine learning models that power pricing, bidding, and decisioning on a cross-border payments platform. This role owns problems end to end, from formulation to production, and partners closely with Product and Backend Engineering.
Key Responsibilities
End-to-End ML Ownership
Own end-to-end ML solutions for pricing, bidding, and risk decisioning.
Formulate model objectives from first principles, including loss functions, constraints, and metrics, and implement them as production-grade services.
Experimentation & Iteration
Design and run experiments, including A/B tests and offline evaluations, and iterate with clear success metrics.
Production Monitoring
Monitor models in production, investigate regressions, and continuously improve performance.
Requirements
Essential
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, plus 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, and 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, and the 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.
What We Offer
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.
Equal Opportunities Statement
Tolken is an equal opportunity employer. We are committed to creating an inclusive environment for all employees.
Company
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