AI-First Data Scientist

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.