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Metro Bangalore, mid-level ML role with generalist E2E skills and known global brand increases applicant competition.
Core ML, MLOps and Spark skills are broadly transferable, though media measurement domain adds moderate specialization.
Many mandatory technical requirements (E2E ML, MLOps, Spark, deployment) make screening highly stringent.
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Lead design and deployment of end-to-end machine learning systems powering Nielsen's intelligent products.
Make architectural and design decisions ensuring scalability, reliability, and efficiency of ML systems.
Own project lifecycles from conception to deployment, including mentoring junior engineers and managing stakeholder communications.
Bachelor's or Master's degree in Engineering, Mathematics, Statistics, or related field.
Proven experience owning and shipping production machine learning systems.
Proficiency in Python, SQL, and distributed computing frameworks like Apache Spark.
Experience with workflow orchestration tools (e.g., Airflow) and MLOps practices.
Experienced in handling large datasets with classification, regression, anomaly detection, boosted models, and deep learning.
Demonstrates strong ownership of entire ML project lifecycle from problem formulation to deployment and monitoring.
Operates well in ambiguous environments and can mentor junior engineers while aligning technical roadmaps with business needs.