Data Architect - Mid-Level

the Client•Framingham, MA

1d•Onsite

About The Position

The Merchandise Operations Management Delivery team is responsible for modernization of Global Merchandise Financial systems, as well as our Pricing and Clearance Management systems. We achieve these goals by implementing off-the-shelf solutions (Oracle Retail), which drive the ability to support key finance processes and markdown strategies. Our business customers rely on the accuracy and integrity of the solutions we provide to manage our merchandise, pay our vendors accurately and in a timely fashion and ultimately report accurate data to Wall Street. As a Data Scientist, you'll be part of a dynamic and collaborative environment that touches every aspect of the business — from product placement and inventory optimization to systems engineering analytics and modeling. You'll work closely with cross-functional partners across product, engineering, and merchandising, providing both strategic insights and ad-hoc data support to fuel smarter decisions.

Requirements

Proficiency in Python, SQL, Unix Shell Scripting (Linux Shell Scripting), and Stored Procedures
Experience with one or more machine learning frameworks and data analysis/visualization libraries in Python (e.g., Pandas, scikit-learn, PyTorch, Matplotlib, Plotly, etc.)
ETL tool experience (Talend, DataStage, or MoveIT Automation preferred)
EDW/DB experience (Snowflake OR Oracle databases preferred)
Experience with CI/CD pipelines
Strong problem-solving skills, attention to detail, and ability to work independently and collaboratively
Highly developed verbal and written communication skills, with the ability to work up and down within the organization to influence others and achieve results

Nice To Haves

Proven application of advanced techniques in a business setting with impactful results
CI/CD platforms (Jenkins, GitHub, Control-M)
Familiarity with data visualization tools such as Tableau and Power BI
Experience designing and evaluating experiments, including A/B testing and other statistical test methodologies
Experience with time series modeling techniques and/or product recommendation systems
Familiarity with data ingestion, transformation, and integration

Responsibilities

Design, develop, and deploy machine learning models and statistical algorithms to solve complex business and IT problems
Analyze large, structured and unstructured datasets to extract meaningful insights and drive data-informed decisions
Build, maintain, and run automated and ad-hoc reports to support business operations and strategic initiatives
Collaborate with cross-functional teams to identify opportunities for leveraging data and fulfill ad-hoc data requests to support timely and informed decision-making
Build and maintain data pipelines and workflows to support scalable analytics, reporting, and model deployment
Communicate technical findings and recommendations clearly to both technical and non-technical stakeholders
Conduct Time-Series, Sequential testing and other experimental designs to evaluate the impact of business and IT initiatives
Provide guidance on technical approaches and best practices