Applied Scientist / Applied ML Engineer

Tolken

Job Overview

Location

Remote

Job Type

Full-time

Full Job Description

📋 Description

• As an Applied Scientist / Applied ML Engineer at Tolken, you will design, build, and deploy machine learning models that directly power pricing, bidding, and decisioning systems for a cross-border payments platform, with end-to-end ownership from problem formulation to production deployment and monitoring.
• Your work will have tangible impact on financial risk, vendor scoring, and pricing optimization at scale, partnering closely with Product and Backend Engineering teams to translate business problems into ML solutions that drive real-world outcomes.
• Day to day, you will formulate model objectives from first principles—defining loss functions, constraints, and success metrics—and implement them using Python, scikit-learn, and either PyTorch or TensorFlow, ensuring models are production-ready and statistically sound.
• You will design and execute rigorous experiments, including A/B tests and offline evaluations, to validate model performance, interpret results with statistical clarity, and iterate based on empirical evidence to continuously improve decisioning systems.
• You will build and maintain data pipelines using tools like Airflow or Spark, leveraging strong SQL skills for feature engineering, and integrate trained models into REST or gRPC APIs within microservice architectures for low-latency, scalable inference.
• You will monitor model performance in production, detect and investigate regressions, diagnose data drift or concept shift, and lead remediation efforts to ensure long-term reliability and trust in ML-driven decisions.
• You will collaborate with cross-functional, distributed teams to align ML initiatives with product goals, communicate technical findings clearly to both engineers and stakeholders, and contribute to a culture of experimentation and data-informed decision making.
• Tolken operates as a high-growth fintech where ML is central to core business logic—you will work alongside strong engineering peers tackling complex optimization problems in pricing, bidding, and risk, with direct influence on architecture and roadmap.
• In this role, you will deepen your expertise in applying ML to real-world financial systems, gain hands-on experience with MLOps practices, model explainability tools like SHAP, and production monitoring systems, while advancing your ability to balance scientific rigor with engineering pragmatism.
• You will have the opportunity to shape how ML is used in cross-border payments—from dynamic pricing strategies to fraud and credit risk scoring—building models that are not only accurate but also auditable, fair, and compliant with financial industry standards.

🎯 Requirements

• 3-7 years of industry experience as an ML Engineer, Applied Scientist, or Data Scientist
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience
• Strong Python proficiency with pandas, NumPy, scikit-learn, and hands-on experience with either PyTorch or TensorFlow
• Solid grasp of ML fundamentals: supervised/unsupervised learning, model evaluation, regularization, feature engineering, and statistical inference
• Proven experience designing ML models from first principles and deploying them to production in batch or real-time settings
• Hands-on experience with data pipelines and ETL using Airflow or Spark, and advanced SQL for feature extraction and transformation
• Experience integrating ML models into REST or gRPC APIs within microservice architectures
• Ability to design, execute, and interpret experiments with statistical rigor, including A/B testing and offline evaluation
• Excellent problem-solving and communication skills, with proven ability to thrive in cross-functional, distributed teams

🏖️ Benefits

• Direct impact: Own core ML models that influence pricing, risk, and vendor decisions at scale in a live fintech product
• High autonomy: Shape model architecture, experimentation roadmap, and technical direction with strong engineering support
• Intellectual challenge: Solve complex optimization problems in dynamic pricing, bidding under uncertainty, and risk decisioning
• Growth environment: Work in a high-growth fintech with exposure to cutting-edge ML applications in payments and financial services
• Technical depth: Gain experience with MLOps, cloud platforms (AWS/GCP/Azure), Docker, model registries, and CI/CD for ML
• Collaborative culture: Partner with skilled peers in a distributed team that values clarity, experimentation, and inclusive innovation

Skills & Technologies

Python

AWS

Azure

GCP

Docker

Data Science

Remote

Degree Required

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Tolken

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About Tolken

Tolken is a company focused on providing innovative solutions within its industry. They specialize in [mention specific area if known, e.g., software development, data analytics, consulting services], aiming to empower clients with cutting-edge technology and expert insights. Their core mission involves [mention mission if known, e.g., driving digital transformation, optimizing business processes, enhancing customer experiences]. Tolken is committed to delivering high-quality products and services, fostering long-term partnerships, and contributing to the advancement of their field through continuous research and development. Their approach emphasizes collaboration, agility, and a deep understanding of client needs to achieve measurable success and sustainable growth.

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