The role is to lead the team to work to turn business questions into data analysis effectively and provide meaningful recommendations. This is a unique hybrid role that will focus on your knowledge of data infrastructure and your ability to drive insights. The job holder will develop models and train them, set department objectives; hire, promote, motivate, train, and incentivize the team; Find and implement new technologies.
- Minimum Qualification:Degree
- Experience Level:Mid level
- Experience Length:2 years
- Architect, build, maintain, and improve new and existing suites of algorithms and their underlying systems,
- Implement end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, performance testing, and A/B testing,
- Utilize your entrepreneurial spirit to identify new opportunities to optimize business processes and improve consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset,
- Work closely with data scientists and analysts to create and deploy new product features,
- Establish scalable, efficient, automated processes for data analyses, model development, validation, and implementation,
- Write efficient and well-organized software to ship products in an iterative, continual-release environment,
- Contribute to and promote good software engineering practices across the team,
- Knowledge sharing with the team to adopt best practices,
- Actively contribute to and reuse community best practices.
- University or advanced degree in engineering, computer science, mathematics, or a related field,
- 2+ years experience developing and deploying machine learning systems into production,
- Strong experience working with a variety of relational SQL and NoSQL databases,
- Strong experience working with big data tools: Hadoop, Spark, Kafka, etc.
- Experience with at least one cloud provider solution (AWS, GCP, Azure),
- Strong experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc,
- Ability to work in a Linux environment,
- Industry experience building innovative end-to-end Machine Learning systems,
- Ability to quickly prototype ideas and solve complex problems by adapting creative approaches,
- Experience working with distributed systems, service-oriented architectures, and designing APIs,
- Strong knowledge of data pipeline and workflow management tools,
- Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation,
- Relevant working experience with Docker and Kubernetes is a big plus.
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