
Price Optimization Engineer
1 week ago
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:
- Develop and implement
price optimization models
leveraging machine learning and econometric techniques. - 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:
- Bachelor's/Master's in Computer Science, Data Science, Economics, or related field.
- 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:
- Experience with
inventory management systems
and supply chain analytics. - Knowledge of
A/B testing
and experimentation frameworks. - Familiarity with
MLOps practices
for production deployment of models.
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