Lead Data Scientist

Job Description

A role with Target Data Science & Engineering means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Statistics, Optimization or Machine Learning teams, you ll be challenged to harness Target s impressive data breadth to build the algorithms that power solutions our partners in in Marketing, Supply Chain Optimization, Network Security and Personalization rely on. Position overview: Develop and deploy scalable deep learning models to improve experience of Target guest Architect and implement large-scale AI systems using test-driven development practices Build NLP technology for query and document analysis, processing, and understandig Create data pipelines for feature/label extraction and generation Conduct data analysis to identify opportunities and improve models Conduct research to advance the state-of-the-art machine learning and NLP technologies Contributing to the operations/building of AI pipelines Adopting Targets infrastructural platforms and operationalize tools and systems around them Mentor and partner with other engineers to develop software that meets business needs Follow agile methodology for software development and technical documentation Innovate constantly and keep systems up to date with current technologies About you: 4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent experience MS in Computer Science, Applied Mathematics, Statistics, Physics or equivalent work or industry experience 6+ years of experience in end-to-end application development, data exploration, data pipelining, API design, optimization of model latency 2 plus years of experience deploying Machine Learning algorithms into production environments - including model and system monitoring and troubleshooting Highly proficient programming in Java/ Scala and/ or Python Good understanding of Big Data tech - specifically Hadoop, Kafka, Spark Solid understanding of data analysis techniques, including data cleaning, preprocessing, and visualization Demonstrated ability collaborating with data scientists, software engineers and product managers to understand the business requirements and translate to machine learning solutions at scale Excellent communication skills with the ability to clearly tell data driven stories through appropriate visualizations, graphs, and narratives Self-driven and results oriented - able to meet tight timelines