Engineer, Machine Learning at Ralph Lauren


Ref #:
5576664
Department:
Information Technology
City:
Nutley
State/Province:
New Jersey
Country:
United States
Company Description
Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, and Club Monaco, among others, constitute one of the world's most widely recognized families of consumer brands.
Position Overview

Who we are:

The Ralph Lauren Corporation, a global leader in luxury fashion and design, is building advanced analytics capabilities to support its Global business in North America, EMEA, and APAC.Our Analytics Team is focused on building high-quality technology solutions to enhance the business & customer experience across channels and geographies.
The Machine Learning (ML) Engineer is an emerging role in Ralph Lauren’s Analytics team, and will play a pivotal role in delivering insights for the most critical data and analytics initiatives for Ralph Lauren.
Purpose & Scope: Based in Nutley, NJ this ML Engineer will work as part of an elite team alongside data engineers and data analysts focused on maximizing value from data while working on high priority business opportunities across all functions and geographies. The ML Engineer will collaborate with line of business users, business analysts and data analysts to deliver insights by implementing various algorithms and ML models.
The ML Engineer will leverage analytical and data engineering skills to solve business problems, unlock opportunities and create new insights.They will identify and explore internal and external data sets.They will leverage predictive and machine learning models to unlock actionable insights with data and inspire data driven actions.
This role will require both creative and collaborative working with IT and the wider business. It will involve evangelizing effective data management practices and promoting better understanding of data and analytics. The ML Engineer will also be tasked with working with key business stakeholders, IT experts and subject-matter experts to plan and deliver optimal analytics and data science solutions.
Essential Duties & Responsibilities

Who we are:

The Ralph Lauren Corporation, a global leader in luxury fashion and design, is building advanced analytics capabilities to support its Global business in North America, EMEA, and APAC.Our Analytics Team is focused on building high-quality technology solutions to enhance the business & customer experience across channels and geographies.
The Machine Learning (ML) Engineer is an emerging role in Ralph Lauren’s Analytics team, and will play a pivotal role in delivering insights for the most critical data and analytics initiatives for Ralph Lauren.
Purpose & Scope: Based in Nutley, NJ this ML Engineer will work as part of an elite team alongside data engineers and data analysts focused on maximizing value from data while working on high priority business opportunities across all functions and geographies. The ML Engineer will collaborate with line of business users, business analysts and data analysts to deliver insights by implementing various algorithms and ML models.
The ML Engineer will leverage analytical and data engineering skills to solve business problems, unlock opportunities and create new insights.They will identify and explore internal and external data sets.They will leverage predictive and machine learning models to unlock actionable insights with data and inspire data driven actions.
This role will require both creative and collaborative working with IT and the wider business. It will involve evangelizing effective data management practices and promoting better understanding of data and analytics. The ML Engineer will also be tasked with working with key business stakeholders, IT experts and subject-matter experts to plan and deliver optimal analytics and data science solutions.
Experience, Skills & Knowledge

What you will be doing (responsibilities):


Analyze Enterprise data to unlock insights:
Move beyond descriptive reporting helping stakeholders identify relevant insights and actions from data.Use regression, cluster analysis, time series, etc. to explore relationships and trends in response to stakeholder questions and business challenges.

Exploratory Data Analysis
: Analyze internal and external datasets using analytical techniques, tools and visualization methods. Ensure pre-processing/cleansing of data, evaluate data points across enterprise landscape and/or external data points that can be leveraged in machine learning models to generate insights.

ML Model Identification and Training
: Identify, test and train machine learning models that need to be leveraged for business use cases. Evaluate models based on interpretability, performance and accuracy as required. Experiment and identify features from datasets that will help influence model outputs.The ML Engineer will determine what models will need to deploy, data points that need to be fed into models and aid in deployment and maintenance of models.

Create visualizations and tell great stories with data:
The ML Engineer must be able to communicate outputs of predictive model via insights in a way that invites understanding and compels action across multiple levels of the organization.

Develop partnerships with Business SMEs and Data Analysts
: Utilize expertise on Ralph Lauren’s data as well as industry best practices and drive innovation and adoption with the business community on advanced analytics.This role will collaborate with Data Analysts and Business SMEs to understand business problems, articulate how algorithms and ML models can be leveraged, build models that generate insights and aid in adoption with business partners.

Educate and train:
The ML Engineer should be knowledgeable about new data initiatives and how advanced analytics will help solve business problems. They will also be responsible for proposing analytical techniques. They will be required to educate counterparts such as data analysts and business analysts in analytical techniques, which make it easier for them to integrate and consume the data they need for their own use cases.

Education and Experience

  • A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field

is required
.
  • An advanced degree in computer science (MS), statistics, applied mathematics (Ph.D.), information science (MIS), data management, information systems, information science (postgraduation diploma or related) or a related quantitative field

is preferred
.
  • The ideal candidate will have a combination of analytical skills, data governance skills, IT skills and Retail industry knowledge with a technical or computer science degree.

At least 3 years or more
of work experience in analytical or business intelligence disciplines including data analysis, visualization, integration, modeling, etc.

E
xperience in working with cross-functional teams and collaborating with business stakeholders in Retail in support of a departmental and/or multi-departmental analytics initiative.

Deep
Retail Industry knowledge or previous experience working in the business would be a plus.

Technical Knowledge/Skills


Strong experience
with analytical methods including regression, forecasting, time series, cluster analysis, classification, etc.

Strong
experience with advanced analytics tools for Object-oriented/object function scripting using languages such as R, Python, Scala, or similar.

Strong
experience with popular database programming languages including SQL, PL/SQL, etc. for relational databases and on NoSQL/Hadoop oriented databases like MongoDB, Cassandra, etc for nonrelational databases.

Strong experience
working with popular data discovery, analytics and BI software tools like MicroStrategy, Tableau, Qlik, PowerBI and others for semantic-layer-based data discovery.Certification in one more of these tools would be a plus.

Strong experience
in working with data science teams in refining and optimizing data science and machine learning models and algorithms.

Basic understanding
of popular open-source and commercial data science platforms such as Python, R, KNIME, Alteryx, others is a strong plus.

Basic experience
in working with data governance, data quality, and data security teams and specifically and privacy and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification.

Demonstrated ability
to work across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service and others.

Adept
in agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse and automation of data flows between data managers and consumers across an organization

Interpersonal Skills and Characteristics


Strong experience
supporting and working with cross-functional teams in a dynamic business environment.

Required
to be highly creative and collaborative. An ideal candidate would be expected to collaborate with both the business and IT teams to define the business problem, refine the requirements, and design and develop data deliverables accordingly. The successful candidate will also be required to have regular discussions with data consumers on optimally refining the data pipelines developed in nonproduction environments and deploying them in production.

Required to have
the accessibility and ability to interface with, and gain the respect of, stakeholders at all levels and roles within the company.
  • Is a confident, energetic self-starter, with strong interpersonal skills. Has good judgment, a sense of urgency and has demonstrated commitment to high standards of ethics, regulatory compliance, customer service and business integrity.

Engineer, Machine Learning
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United States

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