Founding Data Scientist

Book Depot

19d•Onsite

About The Position

At Book Depot Inc., we’re investing in predictive modeling to drive smarter decisions across inventory, pricing, and operations. As our Founding Data Scientist, you will establish our data science capability from the ground up, taking ownership from problem definition to production deployment. This is a hands-on, high-impact role for someone who wants real ownership, autonomy, and the opportunity to build scalable solutions that directly influence business performance. The role offers the chance to build and own the data science function from day one, work on high-impact problems tied to revenue, inventory, and operations, partner directly with business leaders and decision-makers, shape systems, processes, and future team growth, and see your work move from idea to production and drive measurable results. The salary range for this position is $75,000 to $105,000, and this is an in-office role.

Requirements

5+ years of experience in data science, machine learning, or a related field, or equivalent hands-on experience
Proven ability to take models from raw data through to business impact
Strong Python and SQL skills, with experience using tools such as pandas, scikit-learn, and XGBoost
Experience working with relational databases, preferably SQL Server
Understanding of model deployment, pipelines, and production environments
Ability to work independently in a fast-paced, evolving environment
Strong problem-solving skills and a practical, business-focused mindset

Nice To Haves

Experience with cloud environments, particularly Azure
Master’s degree in Data Science, Machine Learning, Analytics, or a related discipline

Responsibilities

Develop predictive models to optimize product pricing across multiple sales channels, maximizing total revenue over the product lifecycle
Forecast demand by channel and recommend inventory allocation strategies to maximize sell-through and revenue
Determine optimal inventory distribution across distribution centers to balance demand and operational efficiency
Evaluate product purchasing opportunities by predicting sell-through and revenue, and recommend optimal purchase pricing
Build recommendation models to generate tailored product catalogs for wholesale customers, improving conversion and sell-through
Optimize inbound production and sort schedules to balance warehouse capacity while maintaining a strong and diverse product catalog
Build and deploy predictive models that improve decision-making across the business
Translate complex business challenges into practical data science solutions
Partner with engineering and DevOps to productionize models and build scalable pipelines
Design experiments and continuously improve model performance using real-world data
Communicate insights clearly to both technical and non-technical stakeholders
Establish best practices for model development, deployment, monitoring, and continuous improvement