Senior Data Scientist - Customer Analytics and Insights

Internal Role Title: Intelligence Engineer • Customer Insights
Squad Assignment: Assigned to the Brain Squad focusing on Customer Insights and Intelligence  
Reports To: First Principles Engineer

Role Purpose & The Value Mandate: The Intelligence Engineer is responsible for transforming raw operational data into actionable customer intelligence that directly drives revenue and profit optimization. This role is not just about building models; it is about providing the company with a clear, quantified understanding of customer behavior, intent, and value. This intelligence serves as the fuel for product development, sales strategy, and overall business decision-making.

Core Accountabilities & Key Performance Indicator (KPI): This role is measured by its success in quantifying the business value derived from its insights.

Primary Accountability

KPI Measurement

Success Definition

Value of Insights Generated

Value of Insights Generated (VIG)

Documented business actions taken based on the IE's analysis, leading to a measurable improvement in the target Business Outcome KPI (e.g., Conversion Rate, Customer Lifetime Value, or Churn Reduction).

Data Integrity & Usability

Data Reliability Score (DRS)

Consistency, accuracy, and usability of the analytical data layers and models deployed to production systems for end-users.

Key Responsibilities (The "How")

Data Analysis and Discovery

• Customer Pattern Identification: Conduct deep-dive analyses on customer behavior, transaction histories, and product usage data to identify anomalies, segment opportunities, and predict churn/loyalty events.
• Hypothesis Testing: Design, execute, and interpret A/B tests or observational studies to validate business hypotheses and quantify the potential impact of new features or marketing campaigns.
• Data Acquisition: Work with the Memory Squad to ensure new data streams required for high-value intelligence (e.g., sentiment data, external market data) are correctly ingested and modeled.
  

Model Development and Deployment

• Predictive Modeling: Develop, train, and deploy production-grade statistical and machine learning models (e.g., churn prediction, propensity-to-buy, customer segmentation) into the operational environment.
• Model Maintenance: Own the monitoring, retraining, and continuous improvement of deployed models to ensure sustained predictive accuracy and relevance (DRS).
• Code Quality: Apply the Intelligence Engineer standard, ensuring all model code is clean, well-tested, documented, and adheres to the Code Quality Score (CQS) standard.

Required Qualifications

• Experience: 5+ years of progressive experience in a data science, quantitative analysis, or advanced business intelligence role, preferably focused on customer-facing metrics (LTV, conversion, retention).
• Technical Proficiency: Expert knowledge of statistical modeling, machine learning techniques, and data manipulation tools (SQL, Python/R). Proficiency in deploying models via cloud services (e.g., Vertex AI, SageMaker, or similar platform tools).
• Business Acumen: Demonstrated ability to understand core business KPIs and translate abstract data insights into concrete, measurable business recommendations.
• Communication: Proven ability to present complex data and model results clearly and concisely to both technical and non-technical executive audiences.
• Mindset: Outcome-driven, rigorous, and obsessed with the quality and reliability of data.

Engineer the intelligence that drives growth.

This is a high-impact role for a Senior Data Scientist who is passionate about moving beyond models to deliver actionable, revenue-driving customer insights.

If you're rigorous, outcome-obsessed, and ready to build the intelligence layer for a dynamic business, apply now. Let's build the future of customer understanding together.