Senior Applied ML Scientist - Scale ROI with Models (Remote)

Hidden Jobs; United States; Remote; USD 138,000 - 189,000; Full time

Benefits offered by this job

Health benefits

Pension plan

Birthday off

Mental health support

WFH reimbursements

WeWork option

Parental leave

Learning budgets

Job summary

StackAdapt seeks a Senior/Staff Applied Machine Learning Scientist to own end-to-end development of production-grade ML models powering advertising effectiveness and ROI at massive scale. Remote-first, open to candidates located anywhere in the UK.

You will lead the creation of advanced ML algorithms, collaborate with data engineers to deploy models, and prototype high-performance solutions using historical data to validate performance and scalability.

Qualifications

• 3+ years of industry experience in applied ML.
• Master's degree or PhD in CS, Statistics, OR, or related field; dual degrees a plus.
• Ability to break ambiguous tasks into actionable steps.
• Strong understanding of statistics, optimization, and ML.
• Proficiency in coding, data structures, and algorithms.
• Enjoy collaborating in a friendly, collaborative environment.

Responsibilities

• Lead creation and optimization of advanced ML algorithms to improve advertising effectiveness and ROI.
• Own end-to-end development of production-grade ML models and deploy them in live systems.
• Prototype and test innovative algorithms and data pipelines using historical data.
• Lead iterative improvements based on data-driven insights.

Skills

Coding proficiency

Statistics knowledge

Problem decomposition

Collaboration

ML fundamentals

Education

Advanced degree in CS/Stats/OR

Tools

Python

TensorFlow

PyTorch

Job description

StackAdapt seeks a Senior/Staff Applied Machine Learning Scientist to own end-to-end development of production-grade ML models powering advertising effectiveness and ROI at massive scale. Remote-first, open to candidates located anywhere in the UK.

You will lead the creation of advanced ML algorithms, collaborate with data engineers to deploy models, and prototype high-performance solutions using historical data to validate performance and scalability.