AI-First Data Scientist

About CSC Generation

CSC Generation is the AI-native holding company re-engineering omni-channel retail. We acquire iconic brands and transform them with Genesis—our operating platform unifying 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 more—premier home and outdoor banners that double as real-world innovation hubs.

CSC Generation continues to grow through M&A, revitalizing companies with strong brand recognition and loyal customers.

The Role: AI-First Data Scientist

We’re hiring an AI-First Data Scientist who combines deep statistical and machine learning expertise with modern MLOps tooling and automation instincts. You will design and deploy production-ready ML solutions, leveraging cutting-edge platforms like AWS Sagemaker, Vertix, and advanced causal inference techniques such as Double Machine Learning (DML). Your work will not just inform decisions—it will automate, scale, and embed intelligence directly into our operating systems.

This is a highly technical, hands-on role with direct business impact. You’ll be embedded with senior operators and report directly to a brand CEO or platform leader, supporting mission-critical decisions and creating AI systems that improve themselves over time.

What you get to do:

• Develop and deploy end-to-end ML pipelines using modern MLOps practices, cloud-native platforms (e.g., AWS Sagemaker), and scalable infrastructure.
• Conduct causal analysis and treatment effect estimation using DML, causal forests, uplift modeling, and other counterfactual inference techniques to guide high-stakes business strategy.
• Build, train, and optimize predictive and prescriptive models for use cases like pricing, promotions, inventory, marketing attribution, and personalization.
• Integrate models into production systems and monitor their performance using advanced observability tools (yes, even Happyface), including diagnosing drift and data quality issues.
• Partner directly with business leaders to translate ambiguous business problems into machine learning frameworks that deliver measurable ROI.
• Collaborate with engineering teams to improve data pipelines, ensure model reproducibility, and maintain version-controlled, CI/CD-enabled ML workflows.
• Continuously research and apply emerging techniques in AI, including generative AI, automated feature engineering, and reinforcement learning.
• Take complex, high-impact problems end to end - from exploration and feature design through model selection, backtesting, and production deployment with clear impact metrics.
• Design robust experiment and quasi-experiment setups (A/B tests, holdouts, staggered rollouts) and recommend approaches when fully randomized tests are not feasible.

What you bring:

• 5+ years of experience in applied data science, machine learning engineering, with a proven track record of deploying ML models into production.
• Master or PhD degree in Data Science, Computer Science, Statistics, Economics, or related quantitative field.
• Expertise in causal inference frameworks—especially Double Machine Learning (DML), A/B testing, uplift modeling, and other counterfactual methods.
• Strong proficiency in Python or R, with hands-on experience in SQL, Jupyter, Git, and cloud ML platforms (AWS Sagemaker experience preferred).
• Familiarity with MLOps tools for experiment tracking, model registry, reproducibility, and automated deployment.
• Experience working with large datasets, distributed computing frameworks, and data engineering best practices.
• Strong experience applying advanced causal and time-series methods in real-world settings, including diagnosing bias, drift, and data quality issues.
• Demonstrated ability to independently take ambiguous, cross-functional problems from zero to a deployed ML solution with clear success metrics and post-launch evaluation.

Why This Role Is Different

Most data scientist roles focus on building models that live in notebooks. This one takes those models all the way to production—where they shape decisions in real time. You won’t just be running experiments—you’ll be building the future of how our business thinks and operates. You’ll integrate AI directly into decision loops, create reusable data science products, and champion a culture of AI-first thinking across the organization.

What’s in It for You?

Joining CSC Generation isn’t just about having a seat at the table—it’s about helping redesign the table entirely. You’ll be challenged, stretched, and supported as you grow faster than you thought possible. In addition to competitive compensation, we offer:

• Executive Access: Work directly with brand CEOs and senior leadership, solving real business problems and earning mentorship from top operators.
• AI-First Skill Building: Get hands-on with the most advanced AI tools in the market. From automation to prompt engineering, you’ll build a modern tech stack that sets you apart in any industry.
• Accelerated Career Path: High performers are quickly entrusted with greater responsibility, new challenges, and leadership opportunities across our portfolio of brands.
• Competitive Benefits: Paid time off policies, 401(k)/RRSP match, medical/dental/vision and a variety of supplemental policies, and employee discounts at our portfolio companies.

Our interview process:

• Step 1: If you align with our vision and meet the qualifications, we’ll reach out to schedule a conversation and introduce CSC.
• Step 2: You’ll complete a short AI or product-building challenge so we can understand how you approach problems and execution.
• Step 3: Participate in deep-dive interviews with CSC leadership focused on your experience, product mindset, and operational thinking.
• Step 4: Offer. We’ll move fast for the right candidate.

Key skills/competency

• Machine Learning Engineering
• Causal Inference (DML, Uplift Modeling)
• MLOps Practices
• AWS Sagemaker
• Python/R Programming
• SQL
• Predictive Modeling
• Data Engineering
• Experiment Design (A/B Testing)
• Problem Solving