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Metro Bangalore, mid-level 5-8 years band, and broad ML/MLOps skillset increase candidate competition.
ML, MLOps, and cloud skills transfer across industries but require domain-specific expertise, giving medium sensitivity.
Explicit 5-8 years requirement plus mandatory hands-on ML, MLOps, cloud, and tooling raises filter strictness.
Lead the translation of complex business challenges into structured data science problems with measurable outcomes.
Develop, monitor, and validate OKRs using statistical techniques to provide actionable insights and track progress.
Collaborate cross-functionally to implement scalable data science solutions, lead modeling decisions, and promote data literacy.
5-8 years of experience including at least 5 years hands-on in data science, analytics, or related fields with recent exposure to agentic AI solutions.
Advanced proficiency in Python or R for data wrangling and statistical analysis.
Experience with statistical modeling, machine learning algorithms, and data visualization tools (Tableau, Power BI, or Matplotlib).
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related field.
Strong expertise in feature engineering, data mining, and validation of performance metrics with focus on balancing computational efficiency and business requirements.
Experienced with big data platforms (Spark, Hadoop) and cloud-based environments (AWS, Azure, Google Cloud).
Familiar with MLOps practices, deployment pipelines, and has exposure to deep learning frameworks and agentic AI for rapid domain adaptation.