Description

What You’ll Do
Design, build, and productionize predictive ML models used in real customer and business workflows
Work with large, cross-system datasets (cloud data lakes, ETL pipelines, transactional systems)
Partner with product, business, and engineering teams to shape ML roadmaps and use cases
Deploy models via APIs, batch jobs, and streaming pipelines
Monitor, retrain, and govern models with a strong MLOps mindset
Communicate insights clearly to both technical and non-technical stakeholders

Required Skills & Experience
4–7 years of experience as a Data Scientist or ML Engineer with a proven track record of deploying models to production
Domain experience in fintech, lending, collections, or CRM/customer engagement (strongly preferred)
Advanced Python skills and hands-on experience with ML/data libraries such as:
scikit-learn, pandas, numpy, XGBoost, PyTorch/TensorFlow, spaCy/NLTK
Strong SQL skills and experience working with large, messy datasets
Hands-on experience with model deployment (REST APIs, Docker, batch & streaming workflows)
Experience with data visualization / BI tools for analytics and business reporting
Practical experience with model versioning, monitoring, retraining, and governance
Excellent communication skills—able to drive discussions, present results, and influence decisions
Self-starter mindset: entrepreneurial, comfortable with ambiguity, and able to prioritize effectively
 

Key Skills

Education

Any Graduate