Job Description

Big Data Machine Learning Engineer
Job Number: 20-06902
 
Eclaro is currently recruiting for a Big Data Machine Learning Engineer for one of their financial services clients in Chicago, IL. 
 
Eclaro’s client is one of the nation’s largest bank-based financial services companies with assets over $130Billion.  This position is a direct hire and will be responsible for leading a large team of UX/UI Designers in a collaborative environment.  
 
Responsibilities:
  • The Client Engineer works both independently and in collaboration with a cross-functional team of Data scientists and solution system architects to effectively develop, deploy, monitor, manage, and support AI/Client models and advanced analytics technology, data infrastructures, and underlying analytics use casesprimarily focused around open source technologies including cloud infrastructures.
  • This individual evaluates short/long-term business needs required to support client business goals and priorities and works to ensure Advanced analytics solutions are built and deployed in an effective and efficient manner on client Enterprise systems.
  • Under the guidance of the Group's Director and in cooperation with partners in decision science, technology, and data the Engineer will coordinate the development of on-premise and cloud-based analytical non-production and production infrastructure and tools providing computational and statistical capabilities to enhance business results and monetize on client data assets for business decision management solutions.
  • The Engineer will be working closely with data scientists, data mining experts, and business partner supporting the design of experiments and analytics, data sampling and mining, verification of data quality and information integrity, and best practices around the development and deployment of predictive/prescriptive models, DevOps operational systems and practices, and data visualization solutions.
  • The Engineer has responsibility for advising data scientists, Agile project teams, and solution architects in the integration of analytical models/methods into decision management solutions.
  • The Engineer will assist peers in best practices and in the selection and integration of appropriate tools to support required analytic products in close coordination with the organization's AI/AutoML analytics, digital intelligence engineers, solution/data architects, data integration developers, and data science community ensuring tight integration of functionality and toolsets.
 
Qualifications:
  • Bachelor's degree in computer science, electrical/electronic engineering or other engineering or technical discipline is required.
  • Minimum of 8 years of experience in IT and Big data software development is required
  • Minimum 3+ Predictive Analytics model implementation experience in production environments using Client/DL libraries like TensorFlow, H20, Pytorch, Sci-kit Learn.
  • Experience in using NLP, Bi/Visual analytics, Graph Databases like Neo4j/Tiger Graph is preferred,
  • Experiences in designing, developing, optimizing and troubleshooting complex data analytic pipelines and Client model applications using Spark, HDFS and other big data related technologies
  • Programming in Python, R or Scala using distributed frameworks like PySpark, Spark, SparkR
  • Working Knowledge in IDE environment/Tools like Jupyter, R Studio, GitHub, Docker, Jenkins
  • Solid knowledge of data warehousing such as Hadoop, MapReduce, HIVE, Apache Spark, as well as cloud base data storage: Google Cloud Storage with various formats (Parquet, JSON, ORC, Avro, delimited)
  • Solid understanding of databases such as DB2, Oracle, Teradata, MySQL, PostgreSQL
  • Extensive Experience with R and Python including language-specific and data science-oriented packages required.
  • Experience with Hadoop and Spark cluster, SparkSQL, Spark Client, and other third-party machine learning algorithms using Scala, PySpark and/or SparkR
  • Experience with Linux/Unix required
  • Exposure to Google Cloud services- GCP or any cloud environment.
  • Working experience on Apache Airflow
  • Experience in enterprise scale analytic solutions development and deployment with high performance, scalability, availability & reliability.
  • Certified Professional Google Data Engineer preferred
  • Candidate must be a self-starter and creative problem-solver with an innovative and curious mindset.
  • Must have a working knowledge of advanced technology uses cases in financial services including machine learning, interactive data visualization, cloud computing, and streaming analytics.
  • Strong communication skills and the ability to interact and collaborate with all levels of the organization.
  • A broad, enterprise-wide view of the business and varying degrees of appreciation for strategy, processes and capabilities, enabling technologies, and governance
  • The ability to recognize pain points within the organization, functional interdependencies and cross-silo redundancies. Those issues may exist in role alignment, process gaps and overlaps, and business capability maturity gaps
  • The ability to apply architectural principles, methods, and tools to business challenges
  • The ability to create capability portfolios and technical roadmaps addressing gaps
  • The ability to understand and recognize the economics of technology and the business goals
  • The ability to perform industry analysis and identify business and technology trends specific to the portfolio
  • The ability to visualize and create high-level models that can be used in future analysis to extend and mature the business architecture
  • The ability to assist business case creation and realization by aligning business goals to organizational capabilities
  • Strong situational analysis and decision-making abilities
  • Financial and/or Banking background preferred
 
If interested, you may contact:
Audrei Cortez
audrei.cortez@eclaro.com
2019423017
Audrei Cortez | LinkedIn

 
Equal Opportunity Employer: Eclaro values diversity and does not discriminate based on Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

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