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Job Description
Structured overview of role & requirementsAbout This Role
Define and translate business problems into analytical use cases with clear KPIs and success criteria.
Develop, validate, and deploy machine learning, time-series, computer vision, NLP, and generative AI models for forecasting, compliance, and workflow automation to improve business decisions.
Manage end-to-end MLOps including data engineering, model deployment on Databricks and Azure, automation, monitoring, and coordination with IT, alongside stakeholder reporting and mentoring junior staff.
Minimum Requirements
Postgraduate degree in Data Science, Big Data Analytics, Statistics, Computer Science, Engineering, or a related quantitative discipline.
3 to 6 years of applied data science experience with at least one deployed production solution.
Experience with data engineering, model development, and deployment on cloud platforms such as Databricks and Azure.
Work Experience Required: 3–6 years of applied data science experience with production deployment.
Ideal Candidate Profile
Experience in power and energy markets, utilities, manufacturing, cement, or aviation domains is preferred.
Familiarity with cloud ML platforms and certifications in Databricks, Azure Data Engineering or AI are a strong plus.
Proven ability to manage Agile project delivery, stakeholder engagement, and mentor junior data scientists in a complex, enterprise environment.