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 experimental design, and predictive modelling while translating complex methodologies into actionable business insights.
Role & responsibilities
Data Science & Modeling (50%)
- Build and deploy machine learning models for pricing optimization, demand forecasting, and promotional response prediction
- Develop predictive models using regression, decision trees, gradient boosting (XGBoost, LightGBM), and neural networks
- Create time series forecasting models (ARIMA, Prophet, LSTM) for demand and revenue prediction
- Build optimization algorithms for pricing, assortment, and trade spend allocation
- Conduct causal inference analysis to measure true promotional incrementality and long-term effects
- Develop customer segmentation using clustering algorithms and propensity models for churn, LTV, and next-best-action
- Perform market basket analysis and product affinity modeling
- Create recommendation engines for product and promotion personalization
- Implement A/B testing frameworks and experimental design for pricing and promotional strategies
- Conduct feature engineering, model validation, and hyperparameter tuning
- Deploy models into production and monitor performance over time
Revenue Growth Management Analytics (30%)
- Develop price elasticity models and optimize price-pack architecture across channels and segments
- Build promotional effectiveness models analyzing lift, ROI, and baseline incrementality
- Create trade spend optimization models to maximize return on investment
- Analyze portfolio performance and build SKU rationalization frameworks
- Support fact-based retailer negotiations with predictive insights and scenario simulations
- Conduct gross-to-net revenue analysis and identify revenue leakage patterns
- Build what-if scenario planning tools for pricing, promotion, and assortment strategies
Visualization & Communication (15%)
- Design Power BI dashboards to communicate model insights and predictions
- Translate complex model outputs into actionable business recommendations
- Present findings to senior leadership with compelling data storytelling
- Write SQL queries to extract and prepare data for modeling
- Create automated reporting for model performance and business KPIs
Collaboration & Mentorship (5%)
- Partner with commercial, finance, and marketing teams to define modeling requirements
- Document model methodologies, assumptions, and technical specifications
- Mentor junior analysts and data scientists on best practices
- Drive continuous improvement in modeling approaches and analytical frameworks
Key Qualifications
Education:Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering, or related quantitative field. Advanced degree preferred.
Experience:3-6 years of experience in analytics or data science, with at least 2 years focused on Revenue Growth Management, pricing analytics, trade promotion optimization, or commercial analytics in Retail/D2C/FMCG/CPG domains
Technical Skills:
- Expert-level Power BI: DAX, Power Query, data modeling, dashboard design, custom visualizations
- Advanced SQL: Complex queries, window functions, CTEs, query optimization, performance tuning
- Python/R Programming: Data manipulation (pandas, numpy), visualization (matplotlib, seaborn, plotly), statistical analysis
- Machine Learning: Scikit-learn, XGBoost, LightGBM, TensorFlow/PyTorch basics
- Statistical Analysis: Regression (linear, logistic, polynomial), hypothesis testing, A/B testing, experimental design
