Data Science/ML Lead
- Act as the lead for data science and ML within the "data as a product" framework.
- Proactively partner with Product Managers to define data product needs for features and experiments.
- Champion the design and architecture of advanced, intelligent data products, including our first ML models, that solve specific problems and unlock new product capabilities. Drive ML Model Development and Production
- Own the end-to-end lifecycle of our ML models, from ideation and experimentation to deployment and ongoing management.
- Establish and champion MLOps best practices to ensure our models are robust, scalable, and reliable.
- Architect and oversee the building of predictive and prescriptive models that inform product strategy and improve user experience. Mentor, Empower, and Elevate the Team
- Serve as the go-to technical expert for data science & machine learning, providing guidance and mentorship to other data scientists and analysts.
- Elevate the data literacy of the wider organisation by coaching product and engineering teams to embrace data ownership and leverage data for decision making.
- Foster a culture of collaboration, ensuring seamless partnership across all teams and acting as a joint owner of the quality, reliability, and security of our data products.
- Proven leadership experience in a data science role, with a track record of driving strategic initiatives and mentoring junior team members.
- Demonstrable experience leading the end-to-end development , deployment, and maintenance of production-grade machine learning models.
- Experience with MLOps principles and tools.
- Advanced proficiency in Python, including deep experience with key machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
- Strong hands-on experience with complex SQL, preferably in an analytical environment like Snowflake.
- Exceptional communication skills, with the ability to translate complex technical concepts into clear, actionable insights for both technical and non-technical stakeholders.
- Data Warehouse: Snowflake
- BI & Visualisation: Power BI
- Languages: SQL, Python
- Cloud Platform: Azure Services (Functions, Data Lake, Data Factory)
- Event Tracking: Rudderstack, Mixpanel
- Other: Git, CI/CD pipelines, modern data stack with an emphasis on reusability, data governance, and performance
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