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Tiger Analytics

Senior Data Scientist - Customer Loyalty

Posted 14 Days Ago
Remote
Hiring Remotely in United States
Senior level
Remote
Hiring Remotely in United States
Senior level
Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.
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Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

We are seeking a Senior Data Scientist – Causal Inference & Customer Loyalty to join a brand-new Business Unit (BU) supporting leading Retail and CPG clients. In this role, you will design, develop, and deploy advanced causal and predictive solutions that drive critical business decisions across customer retention, loyalty program optimization, churn mitigation, and lifecycle monetization.

You will work closely with business stakeholders, marketing leaders, data engineers, and analytics leadership to build foundational data models and scalable algorithms that deliver measurable revenue generation from customer behavior. The ideal candidate combines strong causal inference expertise with hands-on machine learning experience and the ability to translate complex behavioral data into actionable business recommendations.

Key Responsibilities

  • 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
    • 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

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.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

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