AI/ML - Data Engineer (Natural Language), Siri Understanding
Machine Learning and AI
+ 3+ years of professional work experience in data engineering and data analysis
+ Software Engineering proficiency in at least one high-level programming language (Java, Scala, Python or equivalent)
+ Previous work with distributed data technologies (e.g. Hadoop, MapReduce, Spark, Flink, Kafka, etc.) for building efficient & large-scale data pipelines.
+ Proven experience handling relational databases and large scale distributed systems such as Hadoop and Spark along with querying languages including SQL, Hive and SparkSQL
+ Building batch data processing pipelines curating data for data science consumers.
+ Experience strongly preferred in building stream-processing applications using Apache Flink, Spark-Streaming, Apache Storm, Kafka Streams or others.
+ Creativity to engineer novel features and signals, and to push beyond current tools and approaches
+ Demonstrated ability in orchestrating scheduled jobs and optimizing the flow and schedules as needed
+ Initiate and drive projects to completion with minimal guidance in a fast-paced dynamic environment. Ability to work with remote teams on multiple projects/tasks as needed
As a Data Engineer in Siri Natural Language Data Science team, you will understand the evaluation and metrics used to qualify various products, features and ML models. You will support product development, quality and ML model engineers in identifying the root cause of failures by providing relevant data for root cause analysis. You will play a key part in understanding the points of failure and defining metrics that can track them. You should have developed a strong intuition towards understanding various logs of information and translating it to analytical schema. Your curiosity to understand how the product is used and ability to make customer experience delightful by providing data-driven recommendations takes this team a long way.
**Education & Experience**
MS or BS in CS, Engineering, Math, Statistics, or a related field OR equivalent practical experience in data engineering.
+ Nice to Have:
+ * Applied machine learning, natural language processing skills, applied statistics skills, such as hypothesis testing, experimental design and sample size determination
+ * Designing a data warehouse over a complex network of data sources that can cater to all transactional and analytical needs of data consumers (humans and technology)
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