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Tier-1 brand plus metro locations and mid-level demand create moderate applicant competition.
Heavy Big Data, Scala, Spark and ML requirements limit cross-industry transferability.
Multiple mandatory 5+ year requirements and specific Spark/Scala/AWS/ML stack enforce strict screening.
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Design and implement predictive analytics models for data anomaly detection in claims and eligibility datasets within a Big Data platform.
Develop, deploy, and monitor scalable machine learning models to enhance data accuracy and operational efficiency using AWS services and big data frameworks.
Collaborate with cross-functional teams to understand data sources and data quality objectives, document processes, and report analytics findings.
5+ years experience with AWS services (RDS, Lambda, Glue), Apache Spark, Kafka, Spark streaming, Scala, Hive, SQL, NoSQL databases (MySQL, Postgres, Elasticsearch).
Proficiency in programming languages including Python and Scala, with strong SQL and Unix/Linux shell scripting skills.
Bachelor’s degree in Engineering or MCA (B.Tech / B.E / MCA).
Work Experience Required: 5+ years relevant experience in Big Data and AWS environments.
Experienced in end-to-end implementation of data science and machine learning projects on distributed cloud computing environments such as AWS EMR and Spark.
Strong analytical skills with expertise in programming paradigms for batch and stream processing and predictive analytics using time-series, regression, classification, and clustering techniques.
Able to work cross-functionally to translate data quality requirements into operational machine learning solutions in a senior associate level role.