CSC Generation - AI-First Data Scientist
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT
Requirements
• 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). • 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. • 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]. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Responsibilities
• 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.
Benefits
• 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.
No credit card. Takes 10 seconds.