I&F Decision Sci Practitioner Analyst

Key Responsibilities

• Ownership of end-to-end campaign analytics workstreams for global clients, encompassing data science modeling, AI/ML deployment, and segmentation to measurement and insight generation.
• Design, build, and deploy machine learning models and advanced analytics use cases that support growth analytics strategy for CRM & Loyalty engagement.
• Design and build ML models for use cases including propensity scoring, churn prediction, next-best-action modeling, CLTV prediction, and loyalty tier migration forecasting.
• Own the full model lifecycle: problem framing, feature engineering, model development, validation, deployment, and ongoing performance monitoring.
• Partner with Data Analysts and Analytics Managers to productionize models and integrate outputs into CRM journeys and campaign workflows.
• Contribute to the group-level data science framework, focusing on methodology standards, model lifecycle governance, and MLOps practices.
• Design and govern the global control group framework and statistical methodology across markets.
• Translate model outputs into actionable audience briefs and campaign recommendations in partnership with Insights Managers.
• Document model methodology, assumptions, performance benchmarks, and deployment guidelines for client and internal review.
• Conduct CLV, churn, and engagement analyses to identify high-value customer segments and at-risk cohorts.
• Analyze audience, channel, content, conversion, booking, revenue, and customer journey performance.
• Prepare insight narratives, drivers of performance, and optimization recommendations.
• Lead test-and-learn design (A/B tests, hold-out groups) and interpret results to inform future campaign strategy.
• Build and maintain campaign performance dashboards that provide real-time and retrospective views of marketing effectiveness.

Requirements & Tech Stack

• Advanced SQL
• Campaign analytics
• CRM/customer analytics skills
• Proficiency in Python for machine learning and data science, including libraries like scikit-learn, XGBoost, LightGBM or equivalent.
• Experience building and deploying classification, regression, and ranking models at scale.
• Strong understanding of customer analytics methodologies: propensity modeling, CLTV, churn prediction, predictive segmentation.
• Familiarity with MLOps practices: model monitoring, versioning, lifecycle governance, and reproducibility.
• Experience working with CRM and marketing data: SFMC event data, CDP profiles, transactional and engagement signals.
• Understanding of experimental design, control group methodology, and A/B testing statistical frameworks.
• Experience with AI/ML-driven personalization and decision engines.
• Proficiency in Power BI and/or Tableau for dashboard development.
• Hands-on experience with CRM and campaign platforms (e.g., Salesforce Marketing Cloud, Adobe Campaign, Braze).
• Strong knowledge of segmentation methodologies and customer analytics (RFM, CLV, propensity scoring).
• Experience designing campaign measurement frameworks including attribution and incrementality.
• Experience with A/B testing, attribution basics, and funnel analysis.
• Strong communication skills, with the ability to translate complex model outputs into clear business recommendations.
• Experience with agentic AI frameworks and LLM-integrated analytics pipelines.
• Familiarity with Salesforce Einstein Analytics and Data Cloud ML capabilities.
• Knowledge of Tealium CDP data structures and customer profile attributes.
• Certifications in machine learning or cloud ML platforms (e.g., AWS SageMaker, GCP Vertex AI).
• 2+ years in data science, machine learning, or advanced analytics.
• 1+ years in campaign analytics or CRM analytics.
• Proven track record building and deploying predictive models in production environments.
• Experience in CRM analytics, loyalty analytics, customer lifetime value modelling, or marketing science.
• Exposure to automobile, airline, retail, FMCG, or beauty industry.

Qualifications

• Any Graduation
• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related field.