Key Responsibilities:
1. Unstructured Data Strategy & Architecture
- Own the unstructured data analytics strategy for Customer Intelligence, defining how text data is ingested, processed, classified, and reused across the enterprise.
- Design scalable frameworks for handling text from multiple sources, including:
- Complaints and case notes
- Contact centre transcripts and chat logs
- App reviews and digital feedback
- Surveys, open-ended responses, and social media
- Define taxonomies, ontologies, and classification logic to ensure consistency and longitudinal tracking.
2. AI Modeling, NLP & Advanced Text Analytics
- Design and develop predictive models to identify optimal product bundles based on customer behavior, needs, wallet share, demographics, and profitability signals.
- Build predictive revenue and cost models at customer, segment, and product level to support CLV projections across different time horizons.
- Develop propensity and predictive ranking models that determine the most relevant product or action for each customer across channels.
- Build early churn detection models using behavioral, transactional, complaint, and engagement signals to predict atrisk customers before dropout.
- Make informed trade-offs between rule-based, statistical, and ML-based approaches based on scale, accuracy, and interpretability.
- Continuously monitor and refine models to manage drift, bias, and performance.
- Design and implement NLP models for:
- Topic modelling and theme extraction
- Sentiment and emotion analysis
- Intent detection and driver analysis
- Pattern detection and emerging issue identification
3. Insight Generation & Business Translation
- Convert complex NLP outputs into clear insight narratives aligned to customer outcomes and enterprise priorities.
- Link unstructured insights to structured data (e.g. NPS, volumes, complaints, operational metrics) to support root-cause analysis.
- Enable the Customer Intelligence function to move from anecdotal feedback to evidence-based prioritisation.
4. Cross-Functional Collaboration
- Partner with Data Engineering to ensure robust pipelines, storage, and performance of unstructured datasets.
- Work closely with Complaints, Contact Centre, Digital, and Service teams to validate emerging themes and systemic issues.
- Support governance and executive reporting through reliable, explainable insight outputs.
5. Governance, Ethics & Quality Control
- Ensure all unstructured data analytics comply with data privacy, regulatory, and ethical standards.
- Establish quality controls and documentation so outputs are trusted and reusable.
- Maintain transparency and explainability of models used in decision-making.
All the above accountabilities include but not limited to any additional/new tasks or responsibilities assigned by the line Manager.
Education
- Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or related field
- Postgraduate qualification preferred
Work Experience:
- 69 years of experience in NLP, text analytics, applied AI, or unstructured data analytics
- Demonstrated experience working with large, messy, multi-source text datasets
- Experience in financial services, regulated environments, or large enterprises strongly preferred
Technical competencies in:
- NLP frameworks and libraries (e.g. spaCy, Hugging Face, NLTK)
- Python and SQL for data processing and analysis
- Topic modelling, embeddings, clustering, and classification techniques
- Model evaluation, drift monitoring, and explainability
Data pipeline and architecture collaboration (with Data Engineering teams)
