Role & responsibilities :

We are seeking an experienced Data Scientist who will leverage advanced analytics, statistical modeling, and machine learning to drive data-driven decision-making across pricing, demand forecasting, and inventory optimization initiatives. The ideal candidate is proficient in Python, SQL, and modern machine learning techniques, with hands-on experience in forecasting, price elasticity modeling, and causal inference.

- Develop, implement, and optimize predictive models for forecasting demand, pricing, or inventory levels.

- Design and execute pricing and elasticity models to support revenue management and strategic pricing decisions.

- Apply causal modeling and time-series forecasting techniques to evaluate business drivers and predict future outcomes.

- Analyze large-scale datasets to uncover trends, insights, and opportunities for optimization.

- Collaborate with cross-functional teams (business, engineering, finance, and product) to translate data insights into actionable business strategies.

- Build, test, and deploy machine learning models using Python and related libraries (e.g., scikit-learn, TensorFlow, XGBoost, statsmodels).

- Write efficient SQL queries to extract, transform, and analyze large datasets from relational databases.

- Conduct statistical analysis (hypothesis testing, regression, clustering, A/B testing) to support experimentation and model validation.

- Present findings and recommendations to stakeholders through clear visualizations and reports (e.g., Power BI, Tableau, or Python visualization tools).

- Continuously improve analytical methodologies and stay up to date with new techniques in machine learning and statistical modeling.

Preferred candidate profile :

- Bachelors or Masters degree in Statistics, Mathematics, Computer Science, Economics, Engineering, or a related field.

- 5 to 12 years of professional experience in data science, analytics, or machine learning roles.

- Strong proficiency in Python (pandas, numpy, scikit-learn, statsmodels, etc.) for data manipulation and modeling.

- Advanced SQL skills for data extraction, transformation, and performance tuning.

- Deep understanding of statistical modeling, machine learning algorithms, and time-series forecasting techniques.

- Experience with forecasting models (ARIMA, Prophet, LSTM, etc.) and price elasticity or optimization modeling.

- Hands-on experience with causal inference techniques (e.g., difference-in differences, regression discontinuity, instrumental variables).

- Familiarity with optimization frameworks (e.g., linear/non-linear optimization, stochastic optimization).

- Experience with data visualization tools such as Tableau, Power BI, or Python libraries (matplotlib, seaborn, plotly).

- Excellent problem-solving, analytical thinking, and communication skills.