Data Scientist

Position Overview

We are looking for a Data Scientist at the early stages of their career to join our Integrated Business Planning (IBP) Advance Analytics team. This role is ideal for individuals who are passionate about using data to solve real-world problems, uncover insights, and build scalable models to support business decision-making.

You will work closely with cross-functional teams including IT, merchandising, supply chain and external partners and receive guidance while progressively building your understanding of our data, processes, and business context.

Essential Duties & Responsibilities

Prepare datasets for analysis by cleaning, transforming, and validating data using standard techniques. Apply statistical methodologies to conduct empirical analysis and identify data anomalies Design and implement statistical and machine learning solutions for business challenges. (e.g. forecasting, elasticity modeling, and clustering) Apply broader domain knowledge to evaluate and interpret results with greater autonomy. Shape the design and monitoring of experiments and interpret their outcomes with statistical rigor. Drive the development and deployment of scalable model solutions. Develop clear, impactful visualizations and reports to convey findings to stakeholders Collaborate with stakeholders to gather requirements and understand business objectives. Perform a variety of analytical tasks across different projects, adapting to changing priorities. Identify and implement improvements to existing data workflows or analytical approaches.

Experience, Skills & Knowledge

Demonstrated knowledge and expertise equivalent to advanced education in Data Science, Computer Science, Statistics, Mathematics, or related fields. Proven experience in data science, advanced analytics, or quantitative analysis Strong programming skills in Python or R, with experience in data analysis libraries (pandas, scikit-learn) SQL proficiency for data querying and manipulation. Proficiency with data visualization tools (e.g., Microstrategy, Tableau) or dashboarding in Python (e.g., Plotly, Streamlit). Solid foundation in machine learning concepts, statistical methods, and data preparation techniques Comprehensive understanding of experimentation methods, model evaluation, and business metric interpretation. Effective written and verbal communication skills, especially when explaining technical topics to non-technical audiences. Strong collaborative mindset and receptiveness to feedback Demonstrated ability to handle multiple assignments or projects. Familiarity with version control tools (e.g., Git) and Agile work practices. Exposure to cloud platforms (e.g., AWS, GCP, Azure) or big data tools (e.g., Spark) is a plus. Retail/apparel industry knowledge is advantageous