Data Scientist at ManyPets

A day in the life

In this role, you will be at the forefront of our data-driven initiatives, leveraging advanced statistical techniques, machine learning, and artificial intelligence to uncover trends and patterns that inform our business strategy. Your insights will play a key role in shaping decisions across various business areas, including marketing, sales, claims, customer retention, fraud detection, and customer servicing.

As a Data Scientist, you'll collaborate closely with cross-functional teams, including product management and engineering, to integrate your findings into our operations and develop predictive models that enhance our business processes. This collaborative approach allows you to work on a variety of projects, ensuring that your contributions have a significant impact across the organisation.

We value innovation and continuous improvement, so you'll be encouraged to stay current with emerging trends in data science and the pet insurance industry. You'll have the opportunity to evaluate and implement new methodologies, tools, and frameworks to keep our data analysis and modelling processes at the cutting edge.

Your responsibilities

• Leverage advanced statistical techniques and machine learning/AI models to identify patterns and trends in data, providing insights to inform business strategy and decision-making.
• Develop predictive models to support decisions across multiple business areas, including marketing, sales, claims, retention, customer behavior, fraud detection, and customer servicing.
• Collaborate with product management and engineering teams to integrate data-driven insights and deploy predictive models into existing systems and processes.
• Communicate complex models and findings to stakeholders effectively through data visualisation, reports, and presentations.
• Stay updated on emerging trends and technologies in data science and the pet insurance industry, implementing new methodologies, tools, and frameworks to enhance data analysis and modelling processes.
• Perform statistical analysis, machine learning, and data mining to support various business needs.
• Participate in Agile or Kanban methodologies, contributing to a collaborative and flexible team environment.
• Work closely with data engineers to ensure robust data pipelines and data infrastructure to support data science projects.
• Contribute to a fast-paced, scale-up environment by adapting to changing business needs and providing innovative data-driven solutions.
• Maintain a strong understanding of data privacy and security requirements, ensuring compliance with relevant regulations.

Your skills and experience

• You’ll have worked as a Data Scientist before and bring experience in accessing and analysing data using SQL and Python, ideally working with a data engineer.
• Proficiency in statistical analysis, machine learning, and data mining techniques.
• A good understanding of key machine learning models, including Gradient Boosting Machines (GBMs) and Neural Networks.
• Hands-on experience with popular machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
• Deep knowledge of Microsoft Excel in a commercial setting.
• You enjoy being Agile - you should be happy working with Agile or Kanban methodologies. 
• You want to be part of a fast-paced, scale-up environment like ours!  
• Ability to communicate effectively with both technical and non-technical audiences at all levels of the organisation.

The interview process

This position has a three-step interview process, which will be held via Zoom.

• Stage 1 – an informal meeting with our Talent team. We’ll explain more about the role and how your experience matches up with what we need.
• Stage 2 – you’ll meet our Analytics Lead and one of the Engineers in the team. This will be a longer interview up to an hour and we’ll ask you technical competency questions plus you’ll be able to ask us anything you need to know.
• Stage 3 – we will ask you to prepare a response to a technical brief, which you will talk through with the Chief Data Officer and one of the Engineers in the team. We expect this to last about an hour.