Lead Machine Learning Scientist

Job Description

Job title: Lead Machine Learning ScientistLocation: FlexibleRole overviewMarkerstudy Group have a very exciting opportunity for a Lead Machine Learning Scientist to own the development, delivery and deployment of Insurance Claims and Operations use cases.You will have the technical support of an established machine learning function, to then create fully automated machine learning pipelines.You will be supported by an Operations Insight function that have vast experience in the delivery, evaluation, and performance tracking of machine learning models.The role will be working in an exciting, diverse and changeable environment, key stakeholders will be across Broker Services, Customer & Third Party Claims, Counter Fraud and Continuous Improvement.Responsibilities:

• Engagement with key stakeholders to understand their challenges, needs and demonstrate how advanced analytics can drive improvement, from smarter data driven decisions
• For developing and maintaining best practice, covering all aspects of machine learning, ensuring policies and procedures are adhered to
• Create robust high-quality code using test-driven development (TDD) techniques and adhering to the SOLID coding standard
• Report and communicate with the Head of Machine Learning and Head of Operations Insight
• Deploy and maintain machine learning methods in a DevOps / MLOps based machine learning environment
• Tune machine learning methods for optimal performance.
• Deploy and maintain machine learning methods in our machine learning pipelines using robust test-driven development (TDD) coding approaches, using the SOLID software development principles.
• Actively contribute to creating a culture of coding and data excellence
• Lead, mentor, and coach, a small, specialized team of junior machine learning specialists and insight analysts
• Create roadmaps, discover and design new projects that leverage state-of-the art machine learning techniques and capabilities, including the tuning of highly sophisticated machine learning models

Key Skills and Experience:

• Previous experience of delivering Insurance Claims and Operations use cases
• Substantial experience in tuning and deploying machine learning methods
• Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, Clustering, Isolation Forest, SVMs, NLP, LLMs
• Experience in DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks, such as AWS, Databricks, Google Cloud, etc.
• Experience in creating production grade coding and SOLID programming principles, including test-driven development (TDD) approaches
• Experience in programming languages (e.g. Python, PySpark, R, SAS, SQL)
• Experience in source-control software, e.g., GitHub
• Ability to demonstrate that bias and ethics have been considered throughout the model build and deployment
• Ability to track model performance including degradation and provide a clear and concise view on explainability
• Proficient at communicating results in a concise manner both verbally and written

Behaviours:

• A high level of professional/academic excellence, educated to at least a master’s level in a STEM-based or DS / ML / AI / or mathematical discipline
• Collaborative and team player
• Logical thinker with a professional and positive attitude
• Passion to innovate and improve processes