About
Location: Hybrid Model (3 days In month) Duration: Long term The Expertise You Have
Has Bachelor’s or Master’s Degree in a technology related field (e.g. Engineering, Computer Science, etc.). 5+ years of experience in implementing Big data solutions in data analytics space. 1-2 years of experience in developing ML infrastructure and MLOps in the Cloud (AWS preferred). Experience in Object Oriented Programming (Java, Scala, Python), Unix scripting or related programming languages and exposure to some of Python’s ML ecosystem (numpy, panda, sklearn, tensorflow, etc.). Experience with building data pipelines in getting the data required to build and evaluate ML models, using tools like Apache Spark or other distributed data processing frameworks. Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (Cassandra, Elastic search, Graph database), API and in-memory technologies. Strong knowledge of developing highly scalable distributed systems using Open-source technologies. Experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Git), orchestration/DAGs tools (Airflow, Luigi, Kubeflow, or equivalent). Solid experience in Agile methodologies (Kanban and SCRUM). Cloud experience (AWS preferred). The Skills You Bring
You have good technical design and analysis skills. You the ability to deal with ambiguity and work in fast paced environment. Your experience supporting mission critical applications. You are familiar with applied data science methods, feature engineering and machine learning algorithms. Your Data wrangling experience with structured and unstructured data. Your experience building ML infrastructure, with an eye towards software engineering. You have superb communication skills, both through written and verbal channels. You have excellent collaboration skills to work with multiple teams in the organization. Your ability to understand and adapt to changing business priorities and technology advancements in Big data and Data Science ecosystem. The Value You Deliver
Examples of specific problems you’ll help solve – build tools to help detect shifts in data/features used by ML models to help identify issues in advance of deteriorating prediction quality, monitoring the uncertainty of model outputs, automating prediction explanation for model diagnostics. Designing & developing a feature generation & store framework that promotes sharing of data among different ML models, etc. Designing, Building and supporting mission critical applications to provide the best customer experience Exploring new technology trends and leveraging them to simplify our data and ML ecosystem Driving Innovation and implementing solutions with future thinking Effectively partner with applied Data Scientists and lay software foundations upon which models can be built Guiding teams to improve development agility and productivity Resolving technical roadblocks to the team and mitigating potential risks
Languages
- English
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