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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro location, and popular Data Scientist/MLE title drive high applicant density.
Core ML and MLOps skills transfer across industries, though marketplace domain experience is moderately beneficial.
Explicit minimum years plus mandatory ML, big-data, Python and MLOps skills create strict shortlisting filters.
Job Description
Structured overview of role & requirementsAbout This Role
Build and deploy scalable end-to-end machine learning solutions for data products, focusing on data quality and continuous monitoring.
Own the full MLOps lifecycle including model monitoring and lifecycle management, collaborating closely with data engineers and analysts.
Provide thought leadership by consulting with product and business stakeholders to prototype, scale, and operationalize holistic ML solutions.
Minimum Requirements
Bachelor's in Statistics, Economics, Analytics, Mathematics, Computer Science, IT or related field with 4 years' analytics experience OR Master's with 2 years OR 6 years' experience in analytics or related field.
Strong expertise in machine learning, big data technologies (Hadoop, Spark, MapReduce), and programming in Python.
Experience with MLOps, distributed in-memory computing, REST/gRPC web services, and SQL/Hive for big data ecosystems.
Work Experience Required: 4+ years with relevant degrees or 6+ years without specified degree (analytics related). Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced in architecting and deploying ML solutions in production with robust monitoring and instrumentation capabilities.
Comfortable working in a fast-paced, large-scale distributed system environment with cross-functional teams (data engineers, analysts, product).
Proficient in full-stack data science role including code refactoring, collaboration, metric development, and advanced ML/statistics on large datasets.