Machine Learning Engineer

Machine Learning Engineer

Apex Systems has an opening for a Remote Machine Learning Engineer position for a National Technology Products and Services Corporation in Vernon Hills, IL for a 6 month contract to hire position!

The Data Science and Analytics COE is responsible for leading the creation and development of the overall strategy and direction of data science and advanced analytics at the client – including ensuring continuity and seamless extension of existing programs, the development of a short- and long-term vision and roadmap, and defining and institutionalizing the role that data and analytics play throughout the organization as the fuel that drives and shapes the clients priorities and serves as an accelerant for the clients progress. The ML Engineer is a key player in the Integrated Tech Programs & Strategies team. This role will be responsible for data engineering, data science Model deployment, testing and management for the end-to-end ML and data pipeline including data products. This role will leverage the clients AI labs environment to enable the delivery in a common data lake and products. Reporting to the Sr Manager AI Engineering & Architecture of Data Science and Analytics the ML Engineer must have data infrastructure, data engineering and Machine Learning skills, a proven track record of leading and scaling data pipelines, ML Model deployments in a cloud/on prem/big data environment, strong operational skills to drive efficiency and speed. In addition, strong technical leadership skills are required with a vision for how data science can proactively improve company.

Key Areas of Responsibility:

• Responsible for building and managing end-to-end data pipelines and operations from ingestion and integration through delivery for the data science prototypes 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.

Education and/or Experience Qualifications:

• Bachelor’s 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.

Required Qualifications:

• 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++).
• Data Exploration and ETL: Alteryx, Talend, H2O, Informatica, Data Stage, Azure Data explorer, Azure Data Factory.
• Data Warehouse Solutions: Redshift, Snowflake, Postgres, Data Lake.
• Big Data technologies, Azure, AWS, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB).
• Cloud storage: S3, GCS, ADLS, Blob.
• Machine Learning: Cloudera Data Science Workbench, Azure ML, Amazon ML, Google AutoML, Vertex AI.
• Data Visualization Solutions: MS Power BI, Looker, Tableau, Azure Streaming Analytics, Data Lake Analytics, Azure Time Series Insights, Azure Synapse Analytics.
• CI/CD and Code Management: Git, Maven, Docker, Jenkins, Azure Dev Ops.

Preferred Qualifications:

• Experience working with on-prem and cloud-based data warehouses.
• Experience with cloud-based personalization and machine-learning applications.