Principle AI Data Scientist (Local to CO only)

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Principle AI Data Scientist

Role Summary

We are seeking a Senior AI Data Scientist to design, build, and productionize advanced analytics and data science solutions at enterprise (Fortune 100) scale. This role is primarily focused on leveraging AI and ML to deliver business‑critical models and insights, including (but not limited to):

• Propensity and next‑best‑action models
• Churn and retention predictors
• Lead generation and prioritization models
• Competitive intelligence and “save” models that detect churn risk and recommend targeted offers
  
  

You will own solutions end‑to‑end—from “art of the possible” prototypes through rigorous experimentation to robust, scalable production deployments in partnership with AI Engineers and Data Engineers. While this is not a people‑management role, you will provide guidance, mentoring, and training to junior data scientists and analysts, and regularly present your work to senior leaders.

Key Responsibilities

• Design & deliver advanced analytics and ML solutions
• Lead the end‑to‑end development of predictive and prescriptive models (e.g., propensity, churn, lead scoring, competitive response, forecasting, recommendations).
• Translate ambiguous business questions into clear analytical problems, select appropriate modeling approaches, and implement solutions that are deployable in production environments.
• Data science in an AI/LLM environment
• Leverage LLMs and RAG alongside traditional ML to enhance feature engineering, unstructured data understanding, customer insights, and agent‑assist use cases.
• Design prompts, retrieval strategies, and evaluation frameworks for LLM‑powered analytics, while clearly managing risks, limitations, and failure modes.
• Data exploration, feature engineering & experimentation
• Explore large, complex datasets (CRM, billing, interaction/call data, digital, third‑party) to identify drivers of conversion, churn, revenue, and satisfaction.
• Engineer high‑quality features from structured and unstructured data; design and analyze A/B tests and other experiments to validate causal impact.
• Define success metrics, control groups, and experiment designs that stand up to executive and analytic scrutiny.
• Model evaluation, monitoring & governance
• Establish rigorous evaluation frameworks (ROC/AUC, lift, precision/recall, calibration, incremental lift, business KPIs).
• Partner with engineering to implement model monitoring for drift, performance, and stability; contribute to model documentation, governance, and responsible AI practices (bias, fairness, explainability).
• Visualization, storytelling & executive communication
• Create high‑polish data visualizations and dashboards that distill complex model behavior and insights into clear, compelling stories.
• Present confidently to executives, connecting technical work to business outcomes, tradeoffs, and ROI.
• Business partnership & domain focus
• Work closely with Sales, Retention, and Call Center stakeholders to understand workflows, KPIs, and pain points; “see through the eyes” of agents and leaders.
• Shape and prioritize a portfolio of AI/analytics use cases that directly impact revenue, retention, efficiency, and customer experience.
• Collaboration with engineering
• Partner with AI Engineers and Data Engineers to move models from notebook to production—defining data requirements, interfaces, and SLAs.
• Contribute to design of model services, scoring pipelines, and RAG/retrieval layers to ensure solutions are scalable and reliable.
• Mentoring & knowledge sharing
• Mentor junior data scientists and analysts on modeling techniques, experimentation, and best practices in an AI‑heavy environment.
• Document methods, patterns, and lessons learned; help set and maintain high standards for data science craft.
• Adaptability, accountability & execution
• Set your own milestones, manage your workload, and consistently meet or exceed deadlines.
• Own your models and results end‑to‑end, from initial concept through production performance and iteration.
• Operate effectively in rapidly changing, complex environments while maintaining scientific rigor and delivery quality.
  

Required Qualifications

• Education
• Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, Engineering, or a closely related quantitative field.
• Advanced degree (Master’s or Ph.D.) in a quantitative discipline (e.g., Statistics, Applied Math, Computer Science, Economics) strongly preferred.
• Experience
• 10 years of hands‑on applied data science and machine learning experience in industry, building and deploying models that drive measurable business impact.
• 4 years of experience with LLMs and RAG‑based solutions, including prompt engineering and integrating LLMs into analytics workflows.