Data Scientist(Machine Learning | Customer Analytics | Financial Services)

Data Scientist
Machine Learning | Customer Analytics | Financial Services
Job Description | Job Family: Data Science & Analytics
Level: Mid-Senior (5+ years professional experience)
Employment Type: Full-Time
Domain Focus: Banking, Insurance & Financial Services — Customer Analytics, Retention & Risk
Location: Onsite / Hybrid — flexible per engagement
ROLE SUMMARY
We are seeking a results-driven Data Scientist with strong Machine Learning experience to deliver end-to-end data science solutions across banking and insurance — from customer attrition modeling to personalized product recommendations. The ideal candidate combines statistical modeling and applied ML with strong data engineering fundamentals, building scalable data pipelines, predictive models, and production-grade analytics that translate directly into measurable business outcomes. Strong communication skills are essential, as this role requires translating technical insights into actionable recommendations for both technical and non-technical stakeholders.
KEY RESPONSIBILITIES
Design, build, and deploy predictive and rule-based ML models to support customer analytics use cases such as churn/attrition prediction, personalized product recommendations, and targeted marketing.
Apply supervised and unsupervised machine learning techniques, statistical analysis, and text mining to solve complex business problems using structured and unstructured data.
Engineer and maintain scalable data pipelines and feature stores, converting decentralized or manual data-extraction processes into production-ready, automated feature engineering pipelines.
Build and monitor data quality testing frameworks for database migrations and pipeline health, including automated alerting to detect and resolve anomalies quickly.
Develop dynamic Power BI dashboards and reporting layers to track model performance, campaign outcomes, and business KPIs for both operational and executive audiences.
Partner with product, customer experience, and retention teams to translate model outputs and analytical findings into proactive, actionable business strategies.
Establish and maintain coding standards, reusable components (e.g., macros/modules), and version control practices (Git/Bitbucket) to ensure consistency, auditability, and collaboration.
Manage data staging, scheduling, and orchestration across enterprise data platforms (e.g., Teradata, Hive/Hadoop) to ensure reliable, timely data flow into models and reporting.
Support change management and knowledge-sharing initiatives, including onboarding, documentation, and training for team members and stakeholders.
REQUIRED SKILLS & EXPERIENCE
5+ years of professional experience as a Data Scientist / Quantitative Analyst, ideally within banking, insurance, or broader financial services.
Strong hands-on programming skills in Python and/or PySpark, plus SQL for data extraction and transformation.
Practical experience with SAS (Enterprise Guide / Management Console) or equivalent statistical programming tools.
Solid grounding in supervised and unsupervised machine learning techniques, statistical modeling, and text mining / NLP on unstructured data.
Experience building and maintaining data pipelines on enterprise-scale platforms such as Teradata, Hive (Hadoop), or Netezza.
Proficiency with Power BI (including DAX) or a comparable BI tool for building interactive, decision-ready dashboards.
Version control experience with Git/Bitbucket and familiarity with structured development and deployment practices.
Strong communication and stakeholder-management skills, with the ability to translate technical model outputs into clear business recommendations.
Experience working in Agile/Scrum, cross-functional environments involving both technical and non-technical teams.
PREFERRED / NICE TO HAVE
Experience with customer attrition/churn modeling, personalization, or recommendation engines in a retail banking or insurance context.
Experience with rule-based scoring models and feature engineering for customer targeting.
Familiarity with big data tooling (Hadoop ecosystem) and cloud-based data platforms.
Experience presenting analytical findings to senior leadership / executive committees.
Relevant certifications in SQL, data warehousing/ETL, data visualization, or SAS programming.
EDUCATION
Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field; postgraduate qualification an advantage.

Work arrangement
No