# Applied Scientist / Applied ML Engineer - Company: [Tolken]() - Location: India \(Remote\) ยท Remote - Remote: Yes - Team: Engineering - Employment type: Full Time - Posted: March 23, 2026 ## Job description **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** 1. 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. 2. Experimentation & Iteration * Design and run experiments, including A/B tests and offline evaluations, and iterate with clear success metrics. 3. 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. ## Apply [Apply on Tolken]() Canonical job page: