At DICK’S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.
If you are ready to make a difference as part of the world’s greatest sports team, apply to join our team today!
OVERVIEW:
At DICK’S Sporting Goods, we believe sports can change lives. Founded in 1948, DICK’S Sporting Goods first started as a bait-and-tackle shop in Binghamton, NY and has since rapidly expanded into a leading omnichannel retailer with more than 850 locations representing our multiple brands: DICK’S, House of Sport, Golf Galaxy, Public Lands, Going Going Gone, and more. Over the years, it’s been our relentless focus on inspiring, supporting and equipping athletes and outdoor enthusiasts to achieve their dreams that has allowed us to become the $13B company we are today.
Our company is looking to invest in our future as we embark on a journey from being the best sports retailer in the world to becoming the best sports company in the world. We aim to build the ultimate athlete data set that will power our tools and platforms for the most personalized athlete experiences. Join us as we transform our technology, data and analytics to build next-gen tools and platforms for our athletes and teammates. If you are ready to make a difference as part of the world’s greatest sports company, apply today!
Job Purpose:
Drive the build of machine learning products or models that will enable DSG to improve products, solutions and business outcomes. Study product and process-related data, may also be involved in studying environmental conditions, competitor behavior, or other data sources. Leverage advanced knowledge of tools and methods including statistics, artificial intelligence and machine learning; knowledge of coding practices including source control. Translate business questions into data science solutions. Deploy work automations in a technical manner. Translate complex data science findings into language accessible to the business and is able to communicate effectively with technical stakeholders.
Responsibilities:
Data-driven Product and Service Improvement: Develop data-driven analyses to surface new opportunities to differentiate and improve products and user experiences, ensuring consistency across digital products, services, and channels.
Advanced and Predictive Analytics: Drives the application of machine learning techniques, specifically forecasting & optimization methods and build predictive, descriptive, and behavioral models to help achieve various business performance indicators and to help identify business opportunities, linking insights to actionable recommendations.
Advanced Data Collection and Analysis: Conduct advanced research using primary data sources and select information needed for the analysis of key themes and trends.
Data Exploration: Perform complex statistical analysis and utilize mining, modeling, and testing techniques to enable the development and deployment of best-in-class solutions.
Data and Analytics Strategy: Make recommendations to improve data and analytics systems and platforms, contributing to the continuous improvement and refinement of data and analytics strategy.
Performance Improvement through Business Intelligence: Create basic machine learning algorithms and support creation of more complex algorithms that identify patterns in structured data. Partner with different business stakeholders and internal clients to ensure the collection of data in accordance with model standards.
Data Architecture: Oversee data collection mechanisms and how they fit into data architecture, partnering with internal and external stakeholders to ensure quality and accuracy.
Functional/Technical Requirements: Support collection of functional requirements using document analysis and workflow analysis to express requirements clearly and succinctly.
Machine Learning Operations: Use source control systems and write prod supportable code. Deploy machine learning models within a machine learning platform. Research machine learning libraries, models, algorithms, and train/retrain systems. Conduct model experiments, testing, logging, and debugging.
Horizon Scanning: Explore and develop a detailed understanding of external developments or emerging issues and contribute to the evaluation of their potential impact on, or usefulness to, the organization.
Ongoing Learning and Development: Develop own and more junior team member capabilities by participating in assessment and development planning activities as well as formal and informal training and coaching; gain or maintain external professional accreditation where relevant to improve performance and fulfill personal potential. Maintain an understanding of relevant technology, external regulation, and industry best practices through ongoing education, attending conferences, and reading specialist media.
QUALIFICATIONS:
Graduate degree in quantitative fields like statistics, computer science, mathematics, physics, engineering etc.
General Experience: Experience enables job holder to deal with the majority of situations and to advise others (Over 3 years to 6 years)
Managerial Experience: Basic experience of coordinating the work of others (4 to 6 months)
Technical experience: Experience building forecasting & optimization models in conjunction with machine learning models (1+ year)