Data Scientist

Role: Data Scientist

Location: Bengaluru

Experience: 3-5 years

Cloud Platform: Preferably Google Cloud Platform, AWS and Azure

Role Summary

Seeking Data Scientists to develop advanced pricing, demand-forecasting, promotion-

effectiveness, and competitor-pressure models. The role will translate

complex retail data into interpretable recommendations that improve revenue, margin,

price competitiveness, promotional effectiveness, and customer value.

The Data Scientist will work with transaction history, product hierarchies, prices,

promotions, inventory, competitor prices, customer behaviour, seasonality, and external

factors to develop statistically defensible models and actionable insights. Competitor-

pressure modelling may incorporate relative price indices, competitive price gaps,

product-match confidence, price positioning, and competitive-response effect.

Mandatory Skills

Strong Python and SQL skills.

Proficiency in Pandas, NumPy, Scikit-learn, statistical modelling, and

visualization.

Strong foundation in probability, hypothesis testing, confidence intervals,

regression, experimentation, and predictive modelling.

Experience with time-series forecasting, feature engineering, model validation,

and hyperparameter tuning.

Knowledge of Logistic and Linear Regression, Random Forest, XGBoost,

LightGBM, clustering, and ensemble methods.

Understanding of price elasticity, demand response, promotion uplift, and

commercial performance metrics.

Experience handling seasonality, outliers, missing values, sparse data, class

imbalance, and changing data distributions.

Ability to explain analytical findings to commercial and nontechnical stakeholders.

Experience working with large datasets on BigQuery, Spark, or another cloud

data platform.

Preferred Experience

Retail pricing, merchandising, promotion analytics, assortment optimization, or

revenue management.

Causal impact, uplift modelling, Bayesian modelling, or hierarchical forecasting.

Product matching and competitor-price analytics.

Mathematical optimization subject to margin, inventory, competitive, and

business-rule constraints.

Vertex AI, BigQuery ML, or GCP-based model-development experience.

Power BI, Tableau, or Looker for result communication.