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Data Idols are working with an early-stage technology company looking for a Lead Data Scientist to play a key role in the development of its predictive modelling and machine learning capabilities. This is an opportunity to join at an early stage and have a significant influence on both the product and the direction of the business. As the Lead Data Scientist, you'll take ownership of the machine learning and predictive modelling capability at the heart of the business. Working closely with experienced founders and industry experts, you'll design and build scalable forecasting systems that continuously generate, deploy and improve predictive models. You'll work with large-scale data, applying machine learning, experimentation and feature engineering to improve prediction accuracy and drive better customer outcomes. You'll also help shape product strategy, establish data science best practices, and build a critical part of the company's technology platform. This is a high-impact role in a small, ambitious team where your work will directly influence product performance, customer success and the future growth of the business.
Job Responsibility
Take ownership of the machine learning and predictive modelling capability at the heart of the business
Design and build scalable forecasting systems that continuously generate, deploy and improve predictive models
Work with large-scale data, applying machine learning, experimentation and feature engineering to improve prediction accuracy and drive better customer outcomes
Help shape product strategy, establish data science best practices, and build a critical part of the company's technology platform
Requirements
Experience building production machine learning models
Strong Python and modern ML tooling experience
A background in predictive modelling, classification, propensity modelling, churn prediction or related areas
Excellent statistical and analytical thinking
The ability to thrive in a fast-moving, ambiguous startup environment
A desire to own problems end-to-end rather than operate within rigid structures