About the Job

We are seeking a Data Scientist who will lead analytical design, model development, and experimental on across high-impact customer analytics initiatives such as Customer Lifetime Value (CLV), Predictive NPS/CSAT, Churn/Retention, and Customer Segmentation. The ideal candidate has strong hands-on expertise in predictive modelling, large-scale distributed computing (PySpark), and real-world deployment experience. A proven ability to work with large, complex datasets to translate business needs into scalable analytical solutions.

Your Main Duties Will Include:

Customer Analytics & Predictive Modelling

Build and maintain CLV models (historical and/or predictive) incorporating revenue, costs, engagement signals, and churn/retention.
Develop predictive NPS, satisfaction, and churn models to identify high-risk customers and key drivers of experience.
Design customer segmentation (value-based, behavioral, RFM, clustering, predictive) to support targeting, campaigns, and product design.
Ensure all models are robust, monitored, and explainable, with clear links to business objectives and measurable impact.

Data Wrangling & Feature Engineering

Work with large, complex datasets from multiple sources such as CRM, transactional and interaction data, digital journeys, contact center data, and surveys/VoC.
Use PySpark, Python, and SQL to clean, transform, and join data; build scalable feature pipelines.
Partner with engineering to productionize models via batch jobs, APIs, and dashboards; ensure reliability and performance.

Evaluation & Explainability

Define and track relevant metrics (AUC, F1, uplift, calibration, segment performance, stability, etc.).
Use explainability techniques (feature importance, SHAP, or similar) to communicate model behavior clearly.
Contribute to documentation, model monitoring, and retraining plans to sustain performance over time.

Stakeholder Engagement & Storytelling

Translate business questions into clear analytical problems, hypotheses, and success criteria.
Present insights and recommendations to CX, Marketing, Product, Digital, and Operations stakeholders.
Prepare concise decks, summaries, and dashboards to support decisions and drive adoption.

Requirements:

Experience

4+ years of hands-on experience in Data Science and Predictive Modelling within established or high-growth organizations.
Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
Proven experience in end-to-end delivery of data science solutions—from problem framing and data preparation to model deployment and monitoring.
Demonstrated delivery of at least one (production or advanced PoC) of the following:
- CLV / Profitability
- Churn / Propensity
- Predictive NPS / CSAT
- Customer segmentation at scale

Technical Skills (Must-Have)

Strong proficiency in Python (pandas, scikit-learn, MLlib, or similar libraries).
Strong proficiency in PySpark and working with large datasets on distributed platforms.
Advanced SQL skills for complex querying, data transformation, and performance optimization.
Experience using Power BI, Tableau, or similar tools for analytics, dashboards, and data storytelling.
Experience with MLflow (or similar) for experiment tracking, model versioning, and lifecycle management.
Solid understanding and hands-on experience with core Data Science concepts, including:
- Supervised learning: classification, regression, uplift modelling
- Unsupervised learning: clustering, dimensionality reduction
- Feature engineering, model tuning, and validation
Experience working in a cloud or big data environment (Azure, AWS, GCP, Databricks, or similar).

Preferred Qualifications (Bonus)

Experience with Experience Management platforms such as Medallia, Qualtrics, or similar.
Strong practical MLOps habits (monitoring, drift checks, reproducible pipelines, CI/CD collaboration).
Exposure to NLP and text analytics (e.g., survey verbatims, call-center transcripts, complaint logs) applied to customer experience or insight use cases.

Data Scientist