FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying machine learning models in production environments, with a strong focus on optimization, data processing, and collaboration across teams. Proficient in Python and SQL, with experience in real-time data pipelines and large-scale ML systems.
Highest-signal resume keywords
Machine Learning Model DeploymentPython ProgrammingSQL ProficiencyCross-Functional CollaborationBig Data Solutions
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning FundamentalsModel EvaluationOptimization TechniquesData ProcessingProduction Engineering Discipline
Soft Skills
Technical CommunicationCollaboration
Tools & Technologies
KedroAutoGluonPyTorchPolarsBigQueryGCPAirflowSQLMeshDatabricks
Industry Keywords
Ad TechGrowth AnalyticsPersonalizationPerformance Marketing
Tech Stack
Tools & technologiesAirflowBigQueryGoogle Cloud PlatformPythonPyTorchSQL
About the role
Key responsibilities & impact- Design and build a robust marketing platform that reaches the right audience, anywhere and anytime
- Build high-volume services that remain reliable at scale
- Develop big data solutions using open-source frameworks
- Design, train, evaluate, and improve models for deliverability, forecasting, and optimization
- Refine thresholds, calibration, and guardrails to reduce false positives and decision noise
- Build offline and online evaluation workflows tied to measurable business outcomes
- Partner with Product, Project Leads, and platform-focused Machine Learning and Data Engineers to improve service reliability, latency, observability, and data freshness
- Share ownership of production systems, ship model improvements safely, and participate in the on-call rotation
- Operationalize data scientist prototypes into robust, scalable production systems
- Lead deployment, monitoring, and maintenance of machine learning solutions powering campaign optimizations at scale
Requirements
What you’ll need- 5+ years building ML models deployed and operated in production
- Extreme proficiency in technical communication to nontechnical stakeholders
- Excellent applied ML fundamentals, including classification, regression, forecasting, and rigorous evaluation
- Strong understanding of optimization in a business context
- Strong Python and SQL skills with production engineering discipline, including testing, maintainability, and performance
- Experience balancing model quality, system constraints, and speed-to-production
- Strong experience with ownership and cross-functional collaboration
- Experience in ad tech, growth analytics, personalization, or performance marketing
- Proficiency working with real-time or near-real-time data pipelines
- Experience with experimentation frameworks and production model monitoring
- Experience with large-scale data processing and ML systems such as Kedro, AutoGluon, PyTorch, Polars, BigQuery/GCP, Airflow/SQLMesh, and Databricks ecosystems
- Reinforcement Learning experience such as Q-Learning or Multi-Armed Bandits is a plus
Benefits
Comp & perks- People-first company culture
- Named one of Ad Age’s Best Places To Work in 2026
- Remote work arrangement