Applied Scientist / Applied ML Engineer

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. Originally posted on Himalayas