Urgent! Analytics and Data Science - Retail / E-commerce Job | Fornax
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
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Real-time Analytics and Jobs Trends in Vapi, India (Graphical Representation)
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Great news! Fornax is currently hiring and seeking a Analytics and Data Science Retail / E commerce to join their team. Feel free to download the job details.
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An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at Fornax adheres to the cultural norms as outlined by Expertini.
The fundamental ethical values are:The average salary range for a Analytics and Data Science Retail / E commerce Jobs India varies, but the pay scale is rated "Standard" in Vapi. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.
Key qualifications for Analytics and Data Science Retail / E commerce typically include Mathematical Science Occupations and a list of qualifications and expertise as mentioned in the job specification. Be sure to check the specific job listing for detailed requirements and qualifications.
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Here are some tips to help you prepare for and ace your job interview:
Before the Interview:To prepare for your Analytics and Data Science Retail / E commerce interview at Fornax, research the company, understand the job requirements, and practice common interview questions.
Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the Fornax's products or services and be prepared to discuss how you can contribute to their success.
By following these tips, you can increase your chances of making a positive impression and landing the job!
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