Senior Machine Learning Engineer

Harnham; United States; Hybrid; USD 100,000 - 140,000; Full time

Benefits offered by this job

Annual bonus

Flexible remote working

Comprehensive benefits

Learning and development budget

Clear progression opportunities

Job summary

Harnham is seeking a skilled Machine Learning Engineer to tackle real-world fraud detection challenges. In this role, you'll improve existing models and develop innovative solutions using statistical methodologies.

You'll work closely with product and engineering teams in a flexible environment. The role offers competitive compensation, including annual bonuses and comprehensive benefits.

Your experience with large datasets and proficiency in Python/Spark will be key to success.

Qualifications

• Strong commercial experience building and deploying machine learning models in production.
• Deep understanding of statistical modelling and evaluation, especially in imperfect data settings.
• Experience with large scale data and distributed systems.

Responsibilities

• Drive continuous improvement of core fraud detection models.
• Design and evolve experimentation workflows for model iteration.
• Roll out a new ensemble based model architecture.
• Define robust offline evaluation approaches.
• Address complex statistical challenges.
• Collaborate with product teams to develop new ML features.

Skills

Building and deploying machine learning models

Statistical modelling and evaluation

Large scale data processing

Proficiency in Python

Proficiency in SQL

Experimentation frameworks

Collaboration skills

Tools

Spark

Modern ML libraries

Job description

This is an opportunity to tackle some of the most challenging problems in applied machine learning, working on high scale fraud detection systems where data is imperfect and outcomes are uncertain. You will play a key role in evolving core models and experimentation capability within a mature, well invested ML environment.

The Company

They are a multiple PE funding round Fintech operating at global scale, using advanced machine learning to power critical decision making in real time. Their platform processes high volumes of transactions and supports merchants in delivering secure and seamless customer experiences.

The data science and engineering function is central to the product, with strong collaboration between ML, platform engineering and product teams. They have invested heavily in internal tooling, including a proprietary experimentation interface and robust ML infrastructure.

The Role

You will take ownership of end to end machine learning initiatives, with a focus on improving fraud models and enabling faster, more reliable experimentation. Key responsibilities include:

• Driving continuous improvement of core fraud detection models in production


• Designing and evolving experimentation workflows to support high velocity model iteration


• Rolling out a new ensemble based model architecture to improve decisioning performance


• Defining and implementing robust offline evaluation approaches in a low label environment


• Tackling complex statistical challenges such as delayed or missing fraud outcomes


• Partnering closely with product teams to develop new ML driven features for customers


• Contributing to a tech lead driven roadmap within a mature ML platform and tooling ecosystem

Your Skills and Experience

• Strong commercial experience building and deploying machine learning models in production


• Deep understanding of statistical modelling and evaluation, particularly in imperfect data settings


• Experience working with large scale data and distributed systems such as Spark


• Proficiency in Python and SQL, alongside modern ML libraries


• Experience designing experimentation frameworks or working with A B testing and offline validation


• Ability to translate ambiguous business problems into structured ML solutions
  
• Strong collaboration skills, with experience working cross functionally with product and engineering

What They Offer

• Annual bonus and equity package


• Flexible remote working across the United States


• Comprehensive benefits including healthcare, pension contributions and paid leave


• Access to learning and development budget


• Opportunity to work on high impact ML systems with real world outcomes


• Clear progression within a technically strong and collaborative team

How to Apply

If you are interested in applying your machine learning expertise to complex, real world problems at scale, please submit your application.