Data Scientist

Who is this for
If building sophisticated statistical models and conducting rigorous causal analysis to drive business impact excites you, this is your opportunity. Fornax is seeking a Data Scientist who combines advanced analytical techniques with business acumen to solve complex challenges in the Retail domain.

We are looking for a technically proficient data scientist who excels at causal inference, experimental design, and predictive modeling while translating complex methodologies into actionable business insights.

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
  

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
  
• Apply natural language processing (NLP) techniques for sentiment analysis and customer feedback analysis
  

Experimentation & Testing (20%)

• Design and analyze randomized controlled trials (RCTs) and quasi -experimental studies
  
• Implement Bayesian A/B testing frameworks for sequential experimentation
  
• Develop power analysis and sample size calculations for experimental design
  
• Build multi -armed bandit algorithms for dynamic optimization
  
• 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
  

Requirements

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+ 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