About the Role:

We are looking for a highly skilled Data Scientist / Applied ML Engineer with strong experience in retail and consumer analytics. The ideal candidate will have hands-on expertise in building and deploying machine learning solutions that drive customer growth, retention, personalization, and business decision-making at scale.

You will work closely with business, product, and engineering teams to develop advanced analytics models and production-grade ML solutions using modern cloud and data technologies.

Key Responsibilities :

- Design, build, and deploy end-to-end machine learning and analytics solutions for retail/customer use cases.

- Develop predictive and prescriptive models including :

a. Propensity Models

b. Uplift Models

c. Customer Lifetime Value (CLV)

d. Customer Segmentation

e. Recommendation Systems

f. Churn Prediction Models

- Work with large-scale transactional and customer datasets to derive actionable business insights.

- Perform statistical analysis, hypothesis testing, experimentation, and causal inference to solve business problems.

- Ensure model explainability, interpretability, and performance optimization.

- Collaborate with cross-functional teams including Data Engineering, Product, Marketing, and Business stakeholders.

- Build scalable ML pipelines and contribute to production deployment workflows on cloud platforms.

- Optimize SQL queries and data pipelines for performance and scalability.

Required Skills & Qualifications :

- 5 to 7 years of hands-on experience in Data Science / Applied Machine Learning.

- Prior experience in Retail, Consumer, or E-commerce domain is strongly preferred.

- Proven experience building and deploying at least 23 retail analytics models end-to-end.

- Strong programming expertise in Python for machine learning and analytics applications.

- Solid understanding of :

a. Statistical Modeling

b. Machine Learning Algorithms

c. Experimentation & A/B Testing

d. Causal Inference

e. Model Interpretability Techniques

- Strong SQL skills with experience handling large transactional datasets.

GCP & Technical Expertise :

- Hands-on experience with Google Cloud Platform (GCP), including :

a. BigQuery (advanced SQL and performance optimization)

b. Vertex AI / AI Platform (or equivalent ML orchestration platforms)

c. Cloud Storage

d. loud Functions / Cloud Composer (good to have)

Preferred Qualifications :

- Experience working in production ML environments.

- Familiarity with ML lifecycle management and MLOps practices.

- Strong analytical thinking and problem-solving skills.

- Excellent communication and stakeholder management abilities.