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 at‑risk 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:

• 6–9 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)