Senior Machine Learning Engineer - Fraud

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Requirements:

• 4+ years of professional experience training and deploying models that deliver measurable value (regression, clustering, decision trees, cost-sensitive Machine Learning etc with an emphasis on gradient boosting-based methods).
• You have strong SQL skills, confidently able to pull and manipulate data to get into the desired format for modelling (CTEs, joins, case statements, subqueries)
• Possess strong Python skills, able to automate processes and deploy applications. you are able to deploy your stuff and be able to set up at least basic monitoring.
• Familiar with building and deploying web applications using Python web frameworks.
• Experience in one or more of the following areas: Machine Learning (Scikit Learn, XGBoost, H2O etc...), SQL Analytics (BigQuery, Redshift, Databricks, Athena, etc), Visualisation Tools (Mode, matlibplot, seaborn, streamlit Looker, Tableau, Periscope, etc)

Responsibilities:

• Modernization our FinCrime Machine Learning Pipeline
• Evaluate and integrate new data sources for our algorithms, aligning with Data Engineering and Analytical Engineers' best practices for dbt
• In collaboration with Data Scientist, automate the training and deployment of updated models, ensuring the output is tested, scalable and documented and checks are in place to identify drift.
• Help build experiments framework to evaluate new models, third-party data sources and tooling.
• Translate commercial requirements into technical solutions, converting real-world problems into solvable data science projects, resulting in insights that further the strategy and enable visibility into key results
• Improving existing models through greater scrutiny of the methodology and improving the input data
• Develop strategies and tools to help less technical individuals understand and use the models and results.