Analytics and Data Science - Retail / E-commerce

Who is this for

Fornax is seeking an exceptional Data Scientist who combines advanced analytical techniques with strong business acumen to drive measurable impact in the Retail and Ecommerce domain.

This role is ideal for professionals who are passionate about building sophisticated statistical models, conducting rigorous causal analysis, and translating complex methodologies into actionable business insights.

The ideal candidate excels at causal inference, experimental design, and predictive modeling while maintaining the ability to communicate technical concepts effectively to diverse stakeholders across the organization.

Key Responsibilities

Advanced Analytics & Causal Inference (30%)

Design and implement causal inference studies using difference-in-differences (DiD), regression discontinuity, synthetic control methods, and propensity score matching

Conduct rigorous A / B testing and experimental design to measure treatment effects and validate business interventions

Build predictive models using machine learning techniques (random forests, gradient boosting, neural networks) for customer behavior, demand forecasting, and churn prediction

Perform time series analysis and forecasting for sales, inventory, and market trends

Apply advanced statistical methods to identify and quantify causal relationships in observational data

Develop attribution models to measure the incremental impact of marketing campaigns and business initiatives

Develop Marketing Mix Models (MMM) and attribution models to measure the incremental impact and ROI of marketing campaigns across channels, optimize budget allocation, and quantify the contribution of business initiatives

Statistical Modeling & Machine Learning (25%)

Build and deploy supervised and unsupervised learning models for classification, regression, clustering, and recommendation systems

Implement feature engineering pipelines and model selection frameworks to optimize predictive performance

Develop customer segmentation models using clustering algorithms and behavioral analytics

Create price optimization and dynamic pricing models using elasticity analysis

Build survival analysis models for customer lifetime value and retention prediction

Experimentation & Testing (20%)

Design and analyze randomized controlled trials (RCTs) and quasi-experimental studies

Implement Bayesian A / B testing frameworks for sequential experimentation

Create test-and-learn frameworks for rapid business experimentation

Monitor and diagnose experiment validity issues including selection bias, spillover effects, and non-compliance

Strategic Decision Support & Communication (25%)

Translate complex analytical findings into clear, actionable recommendations for business stakeholders

Partner with business leaders to frame strategic questions as testable hypotheses and analytical problems

Create data visualizations and executive summaries that communicate technical insights to non-technical audiences

Develop and maintain strategic KPI frameworks aligned with business objectives

Lead cross-functional analytics projects from problem formulation to implementation

Provide data-driven recommendations for product launches, market expansion, and customer acquisition strategies

Key Skills

Technical Skills

Causal Inference :  Difference-in-differences (DiD), instrumental variables, regression discontinuity design (RDD), propensity score matching, synthetic control methods

Machine Learning :  Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), ensemble methods, deep learning

Statistical Analysis :  Hypothesis testing, Bayesian inference, time series analysis, survival analysis, panel data methods

Programming :  Python (pandas, scikit-learn, statsmodels, PyTorch / TensorFlow) and / or R (tidyverse, caret, causalimpact)

Experimentation :  A / B testing, experimental design, power analysis, multi-armed bandits

Data Tools :  SQL, Git, cloud platforms (AWS / GCP / Azure), visualization tools (Tableau, Power BI, or similar)

Education & Experience

2-4 years of experience in data science, applied research, or analytics consulting, preferably in retail or e-commerce

Proven track record of applying causal inference methods to business problems

Experience collaborating with cross-functional teams and communicating technical concepts to business stakeholders