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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level ML role in a metro with broad requirements and popular Data Scientist title increases competition.
Core ML, MLOps, and modeling skills transfer across industries though sector experience is preferred.
Explicit 5+ years and mandatory ML/MLOps and framework experience raise shortlisting strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Own end-to-end data science lifecycle including data exploration, feature engineering, model development, evaluation, deployment, monitoring, and optimization.
Develop and deploy machine learning models for use cases like classification, regression, clustering, forecasting, anomaly detection, and recommendation.
Collaborate with cross-functional teams and manage production-ready ML solutions including MLOps practices such as model versioning, deployment, monitoring, and governance.
Minimum Requirements
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, AI, ML, Engineering, or related discipline.
5+ years of relevant experience in Data Science, Machine Learning, or advanced analytics.
Proficiency in Python and SQL, and experience with ML libraries like Scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch.
Experience with large-scale data processing platforms, cloud environments, and implementing MLOps practices.
Ideal Candidate Profile
Strong hands-on expertise across the complete data science lifecycle and ability to translate business problems into data science and AI/ML solutions.
Experience with banking, payments, financial services, or large-scale enterprise environments.
Ability to work effectively in Agile and enterprise development environments with strong communication and stakeholder-management skills.