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Tier-1 brand, metro locations and a mid-level, generalist ML/MLOps role drive high candidate competition.
Requires ML/MLOps skills transferable across industries but prefers heavy-industry experience, so medium transferability.
Explicit 3+ years plus mandatory MLOps, cloud, DevOps and backend skills make screening highly selective.
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Collaborate in a multi-disciplinary team to enhance client performance in energy efficiency, product quality, and capacity through data science and analytics.
Work closely with client process engineers and analytics specialists, communicating results to executive audiences and developing innovative AI products and algorithms.
Help codify best practices, influence product roadmap, and may travel locally and internationally to client sites as part of McKinsey’s OptimusAI manufacturing AI solution.
Bachelor's degree in Computer Science, Applied Statistics, Mathematics, Analytics, Engineering, Machine Learning, or equivalent.
3+ years of relevant professional experience in advanced analytics, preferably in heavy industry sectors like Mining, Metals, or Chemicals.
Proficiency in Python coding and experience with cloud computing platforms (AWS, GCP, or Azure).
Strong DevOps and backend engineering skills including CI/CD pipelines, containerization (Docker/Kubernetes), and infrastructure as code (Terraform/Ansible).
Experienced in applying advanced analytics, machine learning, and optimization techniques to solve complex industrial problems.
Capable of operating independently with strong organizational skills and completing projects on time as per scope.
Comfortable working in flexible, agile, and multi-disciplinary teams with strong interpersonal and communication skills, including engaging with senior leadership.