Machine Learning Scientist, Pricing/Personalization

About the Opportunity

The Data Science team at RemoteHunter is looking for two Machine Learning Scientists, Pricing/Personalization to develop and enhance machine learning products. These products aim to improve understanding of demand, refine strategy setting, and strengthen reader-book connections. This role specifically focuses on building models for either book recommendation or market pricing, directly supporting digital discovery, marketing, revenue optimization, and risk mitigation.

Responsibilities

• Own the entire ML lifecycle: problem framing, prototyping, validation, and continuous iteration based on feedback.
• Apply statistical and machine learning best practices for feature development, model training, evaluation, validation, and ongoing maintenance.
• Define clear success metrics and leverage both offline evaluation and online experiments, including A/B testing, to validate performance and monitor quality.
• Collaborate closely with engineering and platform teams to successfully productionize models, covering training, deployment workflows, monitoring, and refresh strategies.
• Proactively diagnose model performance and data quality issues, clearly communicating findings and actionable recommendations to relevant stakeholders.
• Write maintainable, production-quality code, contributing to team standards such as code review, documentation, reproducibility, and robust testing.
• Stay updated on applied machine learning advancements and effectively utilize modern AI tools to accelerate development without compromising product quality.

Requirements

• Master’s degree with 2+ years of applied experience or a PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field.
• Strong proficiency in Python and leading ML frameworks like PyTorch or TensorFlow.
• Solid SQL skills; demonstrated experience working with large datasets for feature development, in-depth analysis, and rigorous validation.
• Proven experience deploying machine learning models to production through methods such as batch scoring, APIs, or seamless downstream integration.
• A solid understanding of experimentation methodologies and robust measurement techniques.
• Ability to effectively use the latest AI tools to develop robust software solutions.
• Strong communication skills, with the ability to clearly translate complex business goals into precise technical solutions and vice versa.
• Familiarity with cloud platforms and modern data/ML tooling (e.g., AWS, Databricks, Docker, Kubernetes, Spark).
• Exposure to MLOps concepts and tools, including model registries, pipelines, monitoring, and ensuring reproducibility.

Preferred Qualifications

For Forecasting specialisation:

• Experience with time series forecasting, causal or market-response modeling, optimization, or risk-aware modeling.
• Familiarity with automated model retraining, comprehensive monitoring, and long-term model maintenance strategies.

For Personalization specialisation:

• Experience with recommender systems, ranking/retrieval, personalization techniques, segmentation, propensity modeling, or targeting strategies.
• Familiarity with experimentation, robust measurement, and online evaluation methods.

Benefits & Perks

• Medical and prescription drug insurance
• Dental and vision coverage
• Health Care/Dependent Care Flexible Spending Account
• Health Savings Account
• Pre-Tax and Roth 401(k) plans
• Short and Long-Term Disability Insurance
• Life and AD&D Insurance
• Commuter benefits
• Student Loan Repayment Program
• Educational assistance
• Generous paid time off

Compensation

The salary range for this position is $130,000 to $175,000, with eligibility for an annual profit award or bonus subject to company results.

Key skills/competency

• Machine Learning
• Python
• SQL
• Deep Learning (PyTorch/TensorFlow)
• Model Deployment
• Experimentation (A/B testing)
• Cloud Platforms (AWS)
• MLOps
• Forecasting
• Recommender Systems