Applied Scientist / Applied ML Engineer

This is a fully remote position, open to applicants in India.

📋 Description

• Complete ownership of end-to-end machine learning solutions.

• Manage all aspects of ML solutions focused on pricing, bidding, and risk decision-making.

• Develop model objectives from fundamental principles, including loss functions, constraints, and metrics, and deploy them as production-ready services.

• Conduct experimentation and iteration.

• Create and execute experiments, such as A/B tests and offline evaluations, while iterating based on clearly defined success metrics.

• Monitor production systems.

• Oversee models in production, analyze regressions, and continually enhance performance.

⛳️ Requirements

• 3-7 years of experience as a Machine Learning Engineer, Applied Scientist, or Data Scientist in an industry setting.

• Bachelor's or Master's degree in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience.

• Proficient in Python, including libraries such as pandas, NumPy, and scikit-learn, as well as at least one of PyTorch or TensorFlow.

• Strong understanding of machine learning principles, including both supervised and unsupervised learning, model evaluation, regularization, feature engineering, and statistics.

• Experience in designing models from foundational principles and deploying them to production, whether in batch or real-time environments.

• Practical experience with data pipelines and ETL tools, such as Airflow or Spark, along with strong SQL skills for feature engineering.

• Familiarity with integrating machine learning into REST or gRPC APIs and microservice architectures.

• Capable of designing and interpreting experiments with statistical rigor.

• Excellent problem-solving and communication skills, along with the ability to collaborate effectively in cross-functional and distributed teams.

🏝️ Benefits

• Engage in real machine learning applications in production with a direct impact on pricing, risk, and vendor decisions at scale.

• Take ownership of core models while having the opportunity to influence architecture and strategic direction.

• Collaborate with strong engineering peers on complex optimization challenges within a rapidly growing fintech environment.