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Position Details: Machine Learning Engineer

Location:	Southfield, MI, 100% Remote
Openings:	5
Salary Range:

Description:

Our Client is:

- 100% REMOTE,
- among Forbes 2022 list of World’s Best Employers
- Resume building opportunity for all contractors

Location: Detroit, MI (possibility of 100% REMOTE)

***ONLY USC, GC holder, H4, or GC EAD

***Please include your LinkedIn profile link, if applicable

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 Client’s AI labs environment to enable the delivery in a common data lake and products.

Responsibilities

• 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.
• 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.
• Contribute and support the development of the overall data science and machine learning strategy and roadmap.

Required Skills

• Bachelor’s degree in computer science, or equivalent IT knowledge/experience.
• 2+ years of relevant work experience in Data Analysis, Data Engineer, Data Science & Data Integration.
• Must have strong data infrastructure, data engineering and Machine Learning skills
• Must have a proven track record of leading and scaling data pipelines, ML Model deployments in a cloud/on prem/big data environment
• Strong knowledge of and experience with reporting packages (Business Objects etc.), databases (SQL etc.), programming (XML, JavaScript, or ETL frameworks).
• Programming languages: 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.
• 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.

Preferred 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.