Data Scientist

We are looking for a Data Scientist. This is a high impact role where the Data Scientist will act as the expert for the entire data science workflow. The candidate will be responsible for data collection and exploration to model development, deployment, and long term monitoring. They will work closely with business leaders, engineers, and industry experts to create predictive models, algorithms, and probability engines that drive real results.

• 5+ years of experience as a Data Scientist working on real-world ML problems.
• Proficiency in writing clean, modular, object-oriented Python code.
• Strong SQL skills and advanced data-wrangling capability, with experience working independently on large and messy datasets.
• Deep understanding of math, statistics, and machine learning techniques including deep learning, NLP, classification, forecasting, and regression.
• Hands-on experience deploying machine learning models into production environments.
• Experience translating business questions into data science problem statements and clearly communicating progress to non-technical stakeholders.
• Strong commercial awareness with an ability to connect analytics to measurable outcomes, trade-offs, and growth opportunities.
• Hands on experience working with Azure based tools such as Azure Machine Learning, Azure Data Studio, and related cloud services for model development, deployment, and monitoring.

• Solve business and customer challenges using advanced AI/ML techniques.
• Build prototypes and scalable AI/ML solutions to be integrated into production software products.
• Collaborate with software engineers, business stakeholders, and product owners in an Agile environment.
• Take full ownership of model outcomes and drive continuous improvement across the model lifecycle.
• End-to-end application and feature development with a production-ready mindset.
• Review code, provide constructive peer feedback, and uphold high engineering standards.
• Engage professionally with business partners, external partners, and key internal contacts.
• Translate complex quantitative findings into actionable insights for both technical and non-technical audiences.
• Break down ambiguous business questions into clear, structured, data-driven problem statements.
• Stay informed on emerging ML/AI research and leverage internal learning resources to innovate.
• Collaborate with other data science and machine learning teams to foster a strong data science culture.
• Maintain clear, thorough, and up to date documentation for models, workflows, and processes to ensure transparency and knowledge sharing.