Senior ML Scientist

Summary

Join Encora as a Senior ML Scientist and be responsible for designing, developing, and maintaining high-quality software solutions. You will collaborate with cross-functional teams, lead technical projects, mentor junior engineers, and improve software development practices. This remote position, based in Peru, Colombia, Costa Rica, or Bolivia, requires expertise in machine learning, reinforcement learning, and pricing algorithms. You will build AI-driven pricing agents and apply various ML techniques to optimize revenue and conversion. The role demands extensive software development experience and proficiency in Python and SQL.

Requirements

• Hold a Bachelor’s degree in computer science, software engineering, or a related field
• Possess extensive experience in software development with a focus on designing and building scalable applications
• Have professional/advanced English skills
• Have 8+ years in machine learning, 5+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence
• Possess expertise in classical ML techniques (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans, with hands-on experience in RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization
• Demonstrate proficiency in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding
• Be proficient in Python and SQL (including Window Functions, Group By, Joins, and Partitioning)
• Have experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch
• Possess knowledge of controlled experimentation techniques, including causal A/B testing and multivariate testing

Responsibilities

• Conceptualize, design, and implement state-of-the-art ML models for dynamic pricing and personalized recommendations
• Develop and apply RL techniques, including Contextual Bandits, Q-learning, SARSA, and concepts like Thompson Sampling and Bayesian Optimization, to solve pricing and optimization challenges
• Build AI-driven pricing agents that incorporate consumer behaviour, demand elasticity, and competitive insights to optimize revenue and conversion