Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier‑1 brand, mid-level generalist ML role in metro attracts many qualified applicants.
Core ML, MLOps, and cloud skills are highly transferable across industries.
Specific ML, MLOps, cloud, and deployment requirements increase selection rigor despite no explicit years.
Lead end-to-end data science projects from problem definition through model deployment and evaluation, ensuring quantifiable business impact.
Develop, validate, and deploy reproducible machine learning models using advanced techniques and established MLOps tools (e.g., MLflow, Kubeflow) with cloud optimization.
Translate complex data insights into actionable business strategies, mentor junior team members, and maintain comprehensive technical documentation for scalability and maintainability.
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related quantitative field.
Proficiency in Python/R and SQL with advanced experience using data science libraries like Scikit-learn and deep learning frameworks such as TensorFlow or PyTorch.
Hands-on experience with cloud platforms and tools for data storage, compute, and model operationalization (e.g., Google Cloud Storage, Vertex AI).
Strong software engineering practices including Git workflows, unit testing, and model validation techniques. Work Experience Required: Not explicitly mentioned in the JD.
Technical leader comfortable bridging between business stakeholders and complex data science/ML environments with proven ability to drive strategic, data-driven decisions.
Experienced in designing scalable, production-ready ML pipelines and cloud resource optimization to ensure reliability and cost efficiency.
Capable of mentoring peers, producing clear technical documentation, and communicating complex model results effectively to both technical and non-technical audiences.