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
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End-to-End ML Ownership
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Own end-to-end ML solutions for pricing, bidding, and risk decisioning.
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Formulate model objectives from first principles, including loss functions, constraints, and metrics, and implement them as production-grade services.
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Experimentation & Iteration
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Design and run experiments, including A/B tests and offline evaluations, and iterate with clear success metrics.
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Production Monitoring
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Monitor models in production, investigate regressions, and continuously improve performance.
Requirements
Essential
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3-7 years of experience as an ML Engineer, Applied Scientist, or Data Scientist in industry.
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Bachelor's or Master's in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience.
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Strong Python skills, including pandas, NumPy, and scikit-learn, plus at least one of PyTorch, TensorFlow.
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Strong ML fundamentals, including supervised and unsupervised learning, model evaluation, regularization, feature engineering, and statistics.
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Experience designing models from first principles and shipping them to production, in batch or real-time.
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Hands-on experience with data pipelines and ETL, such as Airflow or Spark, and strong SQL for feature engineering.
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Experience integrating ML into REST or gRPC APIs and microservice architectures.
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Ability to design and interpret experiments with statistical rigor.
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Strong problem-solving and communication skills, and the ability to work effectively in cross-functional and distributed teams.
Nice to Have
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Optimization, bandits, or decision-making under uncertainty, including dynamic pricing and bid optimization.
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Bidding, auctions, marketplace, or recommendation systems experience.
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Fintech background, including payments, cross-border, lending, trading, or risk and scoring.
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Fraud, AML, credit risk, or vendor risk scoring models.
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Model explainability tooling, including SHAP and feature importance, for auditable decisions.
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Cloud experience (AWS, GCP, or Azure), Docker, and MLOps basics such as model registry and CI/CD.
What We Offer
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Real ML in production with direct impact on pricing, risk, and vendor decisions at scale.
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Ownership of core models with room to influence architecture and roadmap.
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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