
CrossAsyst - Data Scientist - Machine Learning
4 weeks ago
Role : Data Scientist Price Optimization/Inventory Optimization.
Experience : 3+ years.
Location : Bangalore (Work from Office).
Job Description :
We are seeking a highly skilled Price Optimization Engineer/Data Scientist with expertise in Azure cloud, inventory optimization, and price elasticity modeling.
The ideal candidate will be responsible for building scalable pricing solutions, developing optimization algorithms, and leveraging advanced analytics to maximize business outcomes.
You will work closely with cross-functional teams to design and implement pricing models, automate workflows, and generate actionable insights that enhance revenue, improve margins, and balance inventory effectively.
Key Responsibilities :
- Analyze and model price elasticity of demand to recommend data-driven pricing strategies.
- Collaborate with business stakeholders to design inventory optimization frameworks that balance supply,
demand, and profitability.
- Build and deploy end-to-end pipelines on Azure Cloud for data ingestion, transformation, and model
deployment.
- Monitor, evaluate, and continuously improve pricing and inventory optimization models.
- Perform scenario-based simulations to measure the business impact of pricing and stocking decisions.
- Communicate findings and recommendations to business leaders in a clear, actionable manner.
Qualifications :
- 3+ years of experience in data science, optimization, or applied machine learning.
- Hands-on expertise in the Azure cloud ecosystem (Databricks, Azure ML, Synapse, Data Lake).
- Strong programming skills in Python, SQL (R is a plus).
- Experience in optimization algorithms, econometrics, and statistical modeling.
- Proven experience working on price elasticity, demand forecasting, or revenue management.
- Strong business acumen with the ability to translate data into strategic recommendations.
Preferred Skills :
- Knowledge of A/B testing and experimentation frameworks.
- Familiarity with MLOps practices for production deployment of models.
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