Role overview

Senior Machine Learning Engineer

Requirements and responsibilities

Readable role content extracted into sections for faster review.

Your Mission

  • Frame ambiguous business problems as well-posed modeling, inference, or optimization tasks, and choose methods that fit the data and the decision.

Your Mission

  • Design, build, validate, and deploy predictive and decisioning models across areas such as fraud and risk monitoring, customer onboarding and due diligence, pricing, and customer lifetime value.

Your Mission

  • Run rigorous experiments and causal analyses, including A/B testing, uplift modeling, and offline evaluation, to measure whether models actually move the outcomes that matter.

Your Mission

  • Engineer features and build the data pipelines that feed training and serving, with attention to leakage, reproducibility, and data quality.

Your Mission

  • Productionise models with strong attention to validation, backtesting, monitoring, drift detection, and retraining, so performance holds up after launch.

Your Mission

  • Work closely with product managers, engineers, and domain experts to identify where modeling creates value and to integrate models into products and operational workflows.

Your Mission

  • Apply optimization and operations research methods where decisions, not just predictions, are the goal.

Your Mission

  • Contribute to modeling standards, evaluation practices, and reusable tooling across the team.

Your Mission

  • Stay current with developments in machine learning and statistics, and apply new methods where they earn their place.

Requirements

  • Strong foundations in statistics and machine learning, with the judgment to match methods to problems.

Requirements

  • Proficiency in Python and its data and ML ecosystem (for example pandas, scikit-learn, NumPy), and strong SQL.

Requirements

  • Hands-on experience building and deploying machine learning models in production, not only in notebooks.

Requirements

  • Solid command of supervised and unsupervised learning, including methods such as gradient boosting, regularised regression, and clustering, with a clear understanding of model evaluation and overfitting.

Requirements

  • Experience with experimentation and inference, including A/B testing and the basics of causal estimation.

Requirements

  • Experience with cloud platforms and modern engineering practices (CI/CD, APIs, monitoring, infrastructure as code).

Requirements

  • Strong software engineering fundamentals including testing, reproducibility, and maintainability.

Requirements

  • Ability to communicate quantitative findings and their business implications clearly to both technical and non-technical audiences.

Nice to haves

  • Experience building models in regulated industries such as payments, fintech, banking, risk, compliance, or fraud prevention.

Nice to haves

  • Experience with use cases such as:

Nice to haves

  • Fraud detection and anomaly detection

Nice to haves

  • Credit and onboarding risk decisioning

Nice to haves

  • Pricing and customer lifetime value modeling

Nice to haves

  • Churn and propensity modeling

Nice to haves

  • Forecasting and time series

Nice to haves

  • Recommendation and personalisation

Nice to haves

  • Background in operations research, mathematical programming, or stochastic optimization.

Nice to haves

  • Knowledge of MLOps, model lifecycle management, feature stores, monitoring, and governance.

Nice to haves

  • Experience with deep learning frameworks such as PyTorch or TensorFlow where the problem warrants them.

Nice to haves

  • Familiarity with data engineering concepts, analytics platforms, and experimentation frameworks.

Nice to haves

  • Contributions to the ML or statistics community through open source, research, or technical writing.
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Browse stack
FocusSenior Machine Learning EngineerRole area
Seniority signalSeniorCandidate level
StackCI/CD, Python, SQLPrimary skills
Location39 accepted countriesEligibility

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