Principle Data Sc

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
  • Bachelors degree in Statistics Mathematics Computer Science Data Science Engineering or a closely related quantitative field.
  • Advanced degree (Masters 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.