Job Location: Vernon Hills, IL
JOB DESCRIPTION
- Responsible for building and managing end-to-end data pipelines and operations from ingestion and integration through delivery for the data science prototyes and data products.
- Adept at queries, report writing and presenting findings, analyze large complex datasets to extract insights and decide on the appropriate technique.
- Understand and use data and ML fundamentals, including data structures, algorithms, computability and complexity and computer architecture.
- Collaborate with data engineers to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production.
- Provide support to engineers and product managers in implementing machine learning in the product.
- Drive the design, building and launching of new data models and ML/Data pipelines in production.
- Strong knowledge of and experience with reporting packages (Business Objects etc.), databases (SQL etc.), programming (XML, JavaScript, or ETL frameworks).
- Identify, analyze, and interpret trends or patterns in complex data sets.
- Consulting with managers, Product owners to determine and refine machine learning objectives.
- Transforming data science prototypes and applying appropriate ML tools and technologies.
- Research and implement best practices to improve the existing machine learning infrastructure.
- Keeping abreast of developments in machine learning
- Contribute and support the development of the overall data science and machine learning strategy and roadmap.
MINIMUM REQUIREMENTS
- Bachelorโs degree in computer science, Information Systems, or equivalent IT knowledge/experience.
- 2+ years of relevant work experience in Data Analysis, Data Engineer, Data Science & Data Integration.
- Experience working with Data engineering, Data science, ETL teams and managing implementing projects that utilize big data, advanced analytics, and machine learning technologies.
- Hands-on experience in building data and ML pipelines from variety of sources such as data warehouses and in-memory OLAP models, as well as experience in NoSQL/cloud.
- Strong understanding of data, ML Models, Big Data, Relational databases, streaming and batch data processing.
- Knowledge of machine learning evaluation metrics and best practice.
- Strong experience building end-to-end data view with focus on integration.
- Programming languages use (SQL, Spark, Python, R, Jupyter Notebooks, Java, Scala, C++).
DESIRED SKILLS
- Experience working with on-prem and cloud-based data warehouses
- Experience with cloud-based personalization and machine-learning applications
- Experience working for consumer or business-facing digital brands.
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