Senior Data Scientist, Marketing AI #AIDA

What matters most

• End-to-End AI/ML Solution Architecture

  The role requires architecting and productionizing models that integrate into customer-centric decision engines. You must demonstrate the ability to move beyond model development to ensure scalability, explainability, and rigorous governance, particularly when applying Generative AI and LLMs to telco product offerings.

• Marketing Optimization and Experimentation

  A core priority is leveraging behavioral data to drive lifecycle marketing. You will be expected to execute rigorous A/B testing, uplift modeling, and causal inference to optimize campaigns, requiring a strong grasp of experimental design to measure the impact of AI-driven personalization on revenue.

• Technical Leadership and Stakeholder Alignment

  As a senior member, you must bridge the gap between technical execution and business strategy. This involves mentoring junior staff to maintain high code quality and effectively communicating complex analytical findings to senior leadership to influence strategic decision-making.

Likely challenges to think through

• Balancing Innovation with Regulatory Governance

  The challenge lies in deploying advanced Generative AI and LLMs within a highly regulated telecommunications environment. Prepare by being ready to discuss how you implement red-teaming, performance monitoring, and explainability frameworks to satisfy both technical standards and corporate compliance requirements.

• Translating Complex Data into Strategic Value

  You will face the difficulty of distilling high-dimensional behavioral data into actionable insights for non-technical marketing stakeholders. Prepare by practicing how to frame technical model performance metrics in terms of business outcomes like churn reduction or cross-sell propensity.

Topics to prepare

• Causal Inference and Uplift Modeling

  The role requires executing rigorous A/B tests and uplift modeling to optimize marketing campaigns. Exercise: Review the difference between correlation and causation in marketing attribution and practice designing an experiment to measure the incremental impact of a specific telco promotion.

• LLM Governance and Red-Teaming

  The position emphasizes the productionization of Generative AI with a focus on explainability and governance. Exercise: Research frameworks for LLM red-teaming to identify potential biases or security vulnerabilities in customer-facing chatbots and draft a strategy for monitoring model performance post-deployment.

• Customer Lifecycle Analytics

  You will be responsible for churn prediction and cross-sell optimization. Exercise: Map out a typical telco customer lifecycle and identify key behavioral data points that could serve as features for a propensity model to predict churn or product upgrade likelihood.

Skills to revise

• LLM Fine-Tuning and Evaluation

  The role requires deep expertise in fine-tuning and evaluating models like GPT or LLaMA. Exercise: Practice fine-tuning a small open-source LLM on a domain-specific dataset and implement an evaluation pipeline using metrics like ROUGE or BLEU to assess output quality.

• Scalable Data Processing with Spark

  You must handle large-scale telco data using Spark. Exercise: Optimize a Python script that performs heavy data aggregation by refactoring it into a Spark DataFrame operation, focusing on minimizing data shuffling and improving execution time.

• Software Engineering for Data Science

  The role demands high code quality and modular design. Exercise: Take a legacy data science script and refactor it into a modular, testable Python package with clear documentation and unit tests, ensuring it follows version control best practices using Git.

• Prepare a case study on telco-specific churn or propensity modeling

  Develop a structured walkthrough of a past project where you built a predictive model for customer behavior. Focus on the end-to-end lifecycle: data extraction using SQL/Spark, feature engineering for customer lifecycle, model selection (e.g., Gradient Boosted Trees), and how you ensured explainability for non-technical stakeholders.

• Demonstrate Generative AI and LLM application expertise

  Prepare a technical summary of your experience with LLMs, specifically focusing on fine-tuning, prompt engineering, and red-teaming. Be ready to discuss how you evaluate model performance and mitigate risks when deploying Generative AI in a regulated enterprise environment.

• Draft a framework for A/B testing and causal inference

  Create a brief document or presentation outlining your approach to designing rigorous experiments. Include how you handle uplift modeling and causal inference to measure the true impact of marketing campaigns, ensuring your methodology accounts for statistical significance and business scalability.

• Curate examples of technical mentorship and code governance

  Prepare specific anecdotes about how you have mentored junior data scientists. Highlight your process for conducting constructive code reviews, enforcing modular design, and using version control (GitHub/GitLab) to maintain high-quality, reproducible codebases.

• Formulate strategic questions for the AIDA leadership team

  Prepare thoughtful questions regarding the AIDA unit's current priorities, such as: 'How does the team balance the need for rapid experimentation with the requirements for model governance and production stability?' or 'What are the primary data challenges in integrating LLMs into existing customer journey decision engines?'