Interprets and applies data analytics to translate business insights into actionable items.
The Data Science team at Kohl’s develops advanced and scalable algorithms to power our online and in-store business. As a data scientist, you will be working closely with other data scientists, software/machine learning engineers (MLEs), product managers, designers within and between teams, when using state-of-the-art AI/ML (Ensemble and Stacking models, Deep-Learning, Large Language Models, etc), and econometrics techniques to build products that enable the company to make intelligent data-driven decisions.
Your primary focus will be applying various algorithms and models to understand price elasticity of demand as well as demand forecasting for use in pricing optimization for over 18 million product SKUs. You will also focus on A/B testing methodologies to measure associational and causal effects to gain actionable insights on how customers view our pricing and market expansion with the goal of capturing new customers while retaining existing ones. The work for this position includes massively parallel processing (MPP) using Spark for data transformation and statistical modeling on Google Cloud Platform (GCP) using Vertex AI. To be successful in this role, you will be expected to deliver end-to-end products, identifying problems and business value and impact while leveraging agile development lifecycle.
Responsibilities:
Excel at using linear and non-linear models to model price elasticity of demand at different product levels.
Develop strategies that will drive optimal pricing decisions resulting in increases in both revenue and profitability.
Establish key performance tracking metrics, monitor ongoing performance of models, and measure market response at various product levels.
Collaborate with team members, stakeholders, and end users to identify, design, execute and interpret experiments to achieve the desired business outcomes.
Communicate complex ideas and analyses to technical and non-technical people as well as business leaders.
Conduct ad-hoc and fail-fast analyses to answer business questions, support strategic initiatives and explore potential areas of opportunities.
Qualifications:
Deep understanding of modeling, model validation and algorithmic development to solve nuanced problems that may not have off-the-shelf solutions.
Experience with Python and its data science ecosystem (Pandas, Scikit-Learn, Spark, etc.) and working across the software development lifecycle.
Ability to understand business outcomes and translate them into actionable decision solutions.
Think in terms of agile and iterative development and be able to document and communicate throughout a product’s data science lifecycle.
Strong and creative problem solving skills with an emphasis on product development.
Solid business acumen, with the ability to understand stakeholder and end-user needs and translate them into decision and optimization solutions.
Capable of proposing rapid experiments to test the effectiveness of new strategies or initiatives, and iterate quickly.
SQL proficiency or other SQL-like data querying tools and languages.
Experience with AI/ML, statistical, and econometrics modeling for cross-sectional and longitudinal studies and experiments (including time-series and forecast modeling).
Adept at documenting, synthesizing and communicating results at all levels of the organization.
Required:
BS with 5+ years of experience (or MS with 2+ years) in Data Science, Computer Science, Machine Learning, Applied Mathematics, or equivalent quantitative field
Track record of guiding teams through unstructured technical problems to deliver business impact
Full stack experience across data science, analytics, and data engineering
Preferred:
Master's or Ph.D. in a quantitative discipline/field (Operations Research, Engineering, Mathematics, Computer Science, Computational Science, Statistics, etc).
Experience and expertise in price elasticity modeling, pricing recommendations and optimization while considering business constraints.
You have a proven track record as a data scientist who develops, optimizes and scales models into a production environment.
You have a proven track record of developing statistical and advanced AI/ML models that are robust and sensitive to data and concept drift.
You have experience with cloud service providers (CSPs) such as GCP, Amazon Web Services (AWS) and Azure and distributed frameworks like Spark.
You have experience using deep learning frameworks such as PyTorch, Keras, TensorFlow, or one of the many published models “zoos” or “gardens”