Senior Data Scientist – Customer Loyalty

Tiger Analytics

Senior Data Scientist at Tiger Analytics focusing on customer loyalty and causal inference. Designing and deploying advanced models for business-critical decisions in retail and CPG sectors.

Posted 6/16/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

ATS Keywords

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Hard Skills

causal inference modelsuplift modelingsynthetic controldouble machine learningmachine learningforecasting modelspredictive modelsdata modeling frameworksSQLPython

Soft Skills

communicationleadershipanalytical thinkingcollaborationpresentation skills

Tools & Technologies

EconMLDoWhyCausalMLcloud environments

Industry Keywords

customer loyaltycustomer churnbehavioral monetizationCPGFMCGretailconsumer-facing industries

Tech Stack

Tools & technologies

CloudNumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Design, develop, and deploy causal inference models (e.g., uplift modeling, synthetic control, double machine learning) to understand the true drivers of customer loyalty and measure the incremental impact of marketing interventions.
Build robust machine-learning-based forecasting and predictive models for customer lifetime evaluation (LTV) and customer churn.
Establish foundational data modeling frameworks for a brand-new Business Unit, transforming raw transactional data into scalable features.
Analyze complex customer behavior, purchase patterns, and engagement metrics to build strategies for direct revenue generation.
Perform large-scale data extraction, transformation, and analysis using SQL.
Partner with marketing, product, and business teams to understand loyalty requirements and translate business problems into analytical solutions.
Present model insights and recommendations to senior client stakeholders, clearly communicating the difference between correlation and causation.
Lead workshops and customer analytics strategy discussions with clients.
Implement and operationalize models in cloud environments.

Requirements

What you’ll need

6+ years of experience in applied data science or advanced analytics.
4+ years of hands-on experience in customer analytics, customer loyalty programs, churn prediction, or behavioural monetisation.
Strong domain experience in CPG, FMCG, retail, or similar consumer-facing industries.
Advanced proficiency in Python (pandas, NumPy, scikit-learn) and causal inference libraries (e.g., EconML, DoWhy, CausalML).
Strong SQL skills for large-scale data processing and complex data modeling.
Demonstrated experience in building data models and analytics capabilities from the ground up for new business units or initiatives.

Benefits

Comp & perks

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.