BUSINESS INTELLIGENCE ANALYST III

Job Description

At TE we strongly believe that data and analytics are strategic drivers for future success. We are building a world class advanced analytics team that will solve some of the most complex strategic problems and deliver topline growth and operational efficiencies across our business. The Analytics team at TE is part of the TE Information Solutions (TEIS) Organization and is responsible for driving organic growth by leveraging big data and advanced analytics. We are on an exciting journey to build and scale our advanced analytics practice. TE is looking for a Machine Learning (ML) Ops Engineer with experience in defining, building, productionizing, and operating ML workloads. This role is expected to provide thought leadership around architectural best practices by leveraging experience and current industry trends. As a Machine Learning (ML) Ops Engineer, you will be working with data scientists to deploy data science models to our cloud platform using ML and AWS technologies such as Athena, Glue, SageMaker. You will be responsible to orchestrate all the processes from data cleaning, preprocessing, data management, auditing, governance, logging, monitoring, security, model training and deployment in a production environment. You will use your expertise to provide recommendations around security, cost, performance, reliability, and operational efficiency to accelerate projects. The ideal candidate is passionate about data science and stays abreast with the latest developments in the field. You will mature our machine learning engineering processes that are implemented by a toolchain and a guidance framework and build on DevOps guidance to orchestrate components of ML lifecycle. You will be also closely working with leadership teams from the different organization to establish the foundation to create value for diverse business functions such as supply chain, pricing, Industrial IOT & digital factory implementation, digital marketing and sales growth, and will impact business units that span through multiple geographic areas.Primary ResponsibilitiesUnderstand current state architecture, including pain points. Create and document future state architectural options to address specific issues or initiatives using Machine Learning. Innovate and scale architectural best practices around building and operating ML workloads by collaborating with stakeholders across the organization. Develop CI/CD & ML pipelines that help to achieve end-to-end ML model development lifecycle from data preparation and feature engineering to model deployment and retraining. Provide recommendations around security, cost, performance, reliability, and operational efficiency and implement them Provide thought leadership around the use of industry-standard tools and models (including commercially available models and tools) by leveraging the experience and current industry trends. Collaborate with the Enterprise Architect, consulting partners, and client IT team as warranted to establish and implement strategic initiatives. Make recommendations and assess proposals for optimization. Identify operational issues and recommend and implement strategies to resolve problems.You Must Have: 3+ years of experience in developing CI/CD & ML pipelines for end-to-end ML model/workloads development Strong knowledge in ML operations and DevOps workflows and tools such as Git, AWS CodeBuild & CodePipeline, Jenkins, AWS CloudFormation, and others Background in ML algorithm development, AI/ML Platforms, Deep Learning, ML Operations in the cloud environment. Strong programming skillset with high proficiency in Python, R, etc. Strong knowledge of AWS cloud and its technologies such as S3, Redshift, Athena, Glue, SageMaker etc. Working knowledge of databases, data warehouses, data preparation and integration tools, along with big data parallel processing layers such as Apache Spark or Hadoop Knowledge of pure and applied math, ML and DL frameworks, and ML techniques, such as random forest and neural networks Ability to collaborate with Data scientists, Data Engineers, Leaders, and other IT teams Ability to work with multiple projects and work streams at one time. Must be able to deliver results based upon project deadlines. Willing to flex daily work schedule to allow for time-zone differences for global team communications Strong interpersonal and communication skillsCompetenciesValues: Integrity, Accountability,Teamwork, Innovation,

Keyskills :
business intelligencetableausqlreportingsql serverbig datasupply chaindata sciencesales growthapache sparkdeep learningdata cleaningbusiness units