AI-First Data Scientist

CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs.Reports to: VP of Engineering Location: Remote - US
About the Role

As an AI-First Data Scientist at CSC Generation, you will sit at the center of our Genesis platform strategy — building the machine learning and AI systems that power decisions across 13+ brands. This role exists because we are moving beyond one-off analyses and ad hoc models toward a reusable, production-grade data science practice that scales across the entire portfolio. You will own work end-to-end, from problem framing and experimentation through deployment and monitoring, in close partnership with engineering, product, and analytics teams. At six months, you will have models running in production, a repeatable framework others can build on, and a clear line of sight into how your work is moving business metrics. At twelve months, you will be the go-to for scoping and delivering high-impact AI solutions and a contributor to how we evolve our AI capabilities across the organization.

What You'll Do

Design, build, and deploy machine learning models end-to-end — from data exploration and feature engineering through production deployment and monitoring.

Integrate AI and predictive models directly into business decision loops, including pricing optimization, demand forecasting, customer segmentation, and personalization.

Develop reusable data science products and frameworks that can be applied across multiple brands within the CSC Generation portfolio.

Partner with engineering, product, and analytics teams to translate business questions into well-scoped, technically rigorous problem statements.

Evaluate emerging AI tools and methods, recommending and prototyping solutions that advance our Genesis platform capabilities.

Document model logic, performance benchmarks, and deployment decisions to create institutional knowledge and enable cross-team reuse.

Champion AI-first thinking across the organization by leading knowledge-sharing sessions and contributing to internal best practices.

Required Qualifications

4+ years of experience in data science, machine learning, or a closely related applied role.

Demonstrated experience taking ML models from experimentation to production in a business environment.

Proficiency in Python and relevant data science libraries (e.g., scikit-learn, XGBoost, PyTorch, or TensorFlow).

Strong command of SQL and experience working with large-scale structured and unstructured datasets.

Experience with cloud data platforms (e.g., Snowflake, BigQuery, Databricks, or equivalent).

Ability to communicate model outputs and technical tradeoffs clearly to non-technical stakeholders.

Experience applying ML to e-commerce, retail, or consumer data domains (e.g., pricing, demand, churn, or recommendations).

Preferred Qualifications

Experience building or fine-tuning large language models (LLMs) or integrating GenAI APIs into production workflows.

Familiarity with MLOps tooling for model versioning, monitoring, and automated retraining (e.g., MLflow, Weights & Biases, or similar).

Background working in a multi-brand, holding company, or platform environment where solutions must scale across business units.

Experience with real-time or near-real-time model serving infrastructure.

Prior work on AI governance, model explainability, or bias/fairness frameworks.

Why Join

The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.

Impact & Visibility: Your models will run in production across 13+ brands, shaping real business decisions — not sitting in a notebook or a slide deck. Leadership sees and values the work data science does here.

Growth & Learning: You will work at the intersection of classical ML and emerging AI, with direct exposure to LLMs, automation, and a modern data stack being actively built and evolved. You will grow your skills on real, complex problems.

Ownership & Autonomy: You will define how problems get scoped, which approaches get tested, and what gets built. There is no bureaucracy between a good idea and putting it into production.

Competitive Benefits: We offer medical, dental, and vision coverage, a 401(k) with company match, paid time off, and remote work flexibility for US-based employees.

Interview Process

1. Recruiter Screen: A 30-minute conversation with a member of our recruiting team to cover your background, this role, and mutual fit.

2. Hiring Manager Interview: A deeper discussion with the VP of Engineering on your technical approach, past project experience, and how you think about scoping and delivering AI solutions.

3. Technical Assessment: A take-home or live exercise focused on a realistic data science problem — expect to walk through your reasoning, methodology, and tradeoffs.

4. In-Person Interview: An on-site or virtual panel with cross-functional stakeholders from engineering, product, and analytics to assess collaboration, communication, and technical depth.

5. Reference Checks: We will connect with a few professional references to learn more about how you work and the impact you have had.

For US-based candidates, this posting is intended for candidates that reside in the following states: AZ, DE, FL, GA, IN, LA, MI, MS, MO, NV, NC, OK, PA, TN, TX, UT, WV, WI, and WY. Our preference is for candidates who reside near our hubs in Northwest Indiana, Austin, Texas, and Toronto, Ontario. Washington state applicants only: If you believe that this job posting does not comply with applicable Washington state law, please notify us by sending an email to [email protected].