Remote Applied ML Scientist: Elevate Ad ROI

StackAdapt Inc.; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Competitive salary

Retirement plan

Paid time off

Mental health program

Health benefits from day one

Work from home reimbursements

Global WeWork access

Training and onboarding

Parental leave

Social events

Job summary

StackAdapt is a remote-first technology company seeking an Applied Machine Learning Scientist to join our engineering team. You will work on ML algorithms to maximize ROI and advertising performance across a high-traffic platform, collaborating with data engineers to implement novel approaches.

The role emphasizes production-grade coding, prototyping, testing with historical data, and iterative improvement in a friendly, collaborative environment across global campaigns.

Qualifications

• Masters degree or PhD in Computer Science, Statistics, Operations Research, or a related field.
• Ability to take an ambiguously defined task and break it down into actionable steps.
• Strong understanding of statistics, optimization and machine learning.
• Proficient in coding, data structures, and algorithms.
• Enjoy working in a friendly, collaborative environment with others.

Responsibilities

• Innovate ML algorithms to maximize ROI and advertising performance.
• Write production code and collaborate with Data Engineers to implement ML algorithms.
• Prototype algorithms and pipelines, test with historical data, and iterate based on insights.

Skills

ML algorithms

Production code

Statistics/Optimization

Programming

Team collaboration

Education

Master's/PhD in CS/Statistics/OR

Tools

Python

TensorFlow/PyTorch

SQL

Job description

StackAdapt is a remote-first technology company seeking an Applied Machine Learning Scientist to join our engineering team. You will work on ML algorithms to maximize ROI and advertising performance across a high-traffic platform, collaborating with data engineers to implement novel approaches.

The role emphasizes production-grade coding, prototyping, testing with historical data, and iterative improvement in a friendly, collaborative environment across global campaigns.