Data Scientist / Machine Learning Engineer

About the Role

We are looking for a Data Scientist / Machine Learning Engineer to build and deploy high‑impact ML solutions that directly influence business metrics such as revenue, customer experience, and risk mitigation.

This is a product‑focused role, where you will work closely with Product and Engineering teams to identify opportunities, build models, and ship them into production at scale.

Responsibilities

• Partner with Product teams to identify high‑impact ML use cases tied to business outcomes
• Build and deploy ML models for:
  • Pricing and discount optimization
  • Fraud detection and risk modeling
• Develop and productionize models using APIs, CI/CD pipelines, Docker
• Work on cloud platforms (AWS / GCP) for scalable ML deployment
• Build monitoring systems for model performance, data drift, and business KPIs
• Collaborate closely with engineering teams to ensure scalable implementation

Requirements

• 3+ years of experience as a Data Scientist / Machine Learning Engineer
• Strong programming skills in Python
• Solid understanding and hands‑on experience with:
  • Classical ML algorithms (regression, decision trees, gradient boosting)
• Hands‑on experience in at least 2+ ML use cases:
  • Fraud / risk modeling
• Strong experience in NLP:
  • Feature extraction from unstructured data
• Experience with productionizing ML models (APIs, Docker, CI/CD)
• Hands‑on exposure to AWS / GCP environments
• Profiles with only academic/research experience (no production ML)
• Candidates from pure financial/banking domains
• Candidates without hands‑on model deployment experience
• Work on real‑world ML problems with direct business impact
• Opportunity to build and scale production‑grade ML systems