ML Researcher, Predictive Modeling & Ranking
Upper-Intermediate English
Middle
Ukraine
UTC+02:00
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Summary
ML Researcher / Engineer wanted to join a high-impact AI/ML team in performance marketing. The company runs campaigns across 25+ verticals and you'll be researching, inventing and implementing suitable ML models into campaign management, and porting successful pipelines and processes into a fully automated ML-driven system. Your models will influence real bidding, real budgets, and real campaign decisions.
Are you a talented developer looking for a remote job that lets you show your skills and get decent compensation? Join Upstaff.com, a platform that connects you with hand-picked startups and scale-ups in the US and Europe.
Required Skills
Nice to Have
What's the project about?
You'll be working with ML models for performance-based (pay-per-lead) marketing platform that relies on prediction, scoring, and ranking. You’ll work with rich tabular data, build models that power optimization loops, and collaborate closely with business and Data Engineering teams to prepare your models for production, not just notebooks.
Required Skills:
- Experience building and validating tabular ML models
- XGBoost/LightGBM/CatBoost and similar models, corresponding Python libraries
- Defining metrics and design validation setups
- Confident feature engineering and data quality/wrangling
- Data pipelines
Would be a Plus:
- Experience with model serving (FastAPI, Bento, TorchServe)
- Monitoring: drift, data checks, stability
- Background in ranking/optimization models
What will you do?
- Build predictive, scoring, and ranking models directly impacting client campaigns and ROI
- Own dataset preparation, pipelines, processes and validation strategies
- Design experiments that challenge assumptions and uncover real lift
- Collaborate with Software and Data Engineers on feature availability and consistency, packaging and preparing models for real usage in production
Who you are?
You have strong, hands-on experience with tabular ML (XGBoost, LightGBM, CatBoost), you’ve trained and validated models, and you enjoy shaping experiments that prove whether a hypothesis lives or dies. You think clearly, value clean data, honest metrics, and production impact.