About the Role
An Experienced Data Scientist is sought to build and deploy scalable machine learning solutions, driving measurable business impact across fintech use cases, including credit risk, fraud detection, customer segmentation, recommendations, and operational forecasting. This role demands strong applied ML expertise, production deployment capabilities, and effective communication of insights to business and product stakeholders.
Key Responsibilities
- Build and deploy machine learning models for classification, regression, clustering, and anomaly detection use cases
- Design end-to-end ML pipelines including data preparation, feature engineering, training, evaluation, deployment, and monitoring
- Develop and deploy model-serving APIs using Fast API and integrate with business systems
- Build backend integrations using Django where required
- Work on modern AI applications including Retrieval-Augmented Generation (RAG) and agent-based workflows
- Conduct experimentation and A/B testing to support product and operational optimization
- Translate business problems into data science solutions with clear success metrics
- Present insights through structured storytelling, dashboards, and stakeholder-ready summaries
Required Technical Skills
- Advanced SQL (complex queries, optimization, analytical functions)
- Strong Python (pandas, numpy, scikit-learn, ML libraries)
- Experience with Django and FastAPI
- Strong understanding of ML pipelines and production deployment workflows
- Experience deploying ML applications on cloud platforms (AWS/GCP/Azure)
- Familiarity with CI/CD and containerized deployments (preferred)
Model & AI Knowledge
- Supervised learning (classification, regression)
- Unsupervised learning (clustering, segmentation)
- Neural networks and deep learning fundamentals
- Model evaluation, interpretability, and performance monitoring
- RAG systems (embeddings, retrieval strategies, evaluation)
- Agent-based AI workflows and orchestration
Statistical Knowledge Expectations
- Probability, distributions, and sampling methods
- Hypothesis testing, confidence intervals, and statistical significance
- Bias-variance tradeoff and model generalization concepts
- Experiment design and A/B testing frameworks
- Regression assumptions, diagnostics, and error analysis
- Metrics knowledge (ROC-AUC, F1, precision/recall, RMSE, MAE, log loss, etc.)
Soft Skills & Expectations
- Strong problem-solving and ownership mindset
- Ability to work with cross-functional teams (product, business, engineering)
- Strong communication and stakeholder influence skills
- Ability to explain complex models in simple, business-friendly terms
- Strong visual storytelling and presentation skills
- Ability to work in a fast-paced environment with multiple priorities
Preferred Background
- Experience in fintech, lending, fraud/risk analytics, payments, or credit scoring
- Experience working with large datasets and scalable analytics environments
Why Join Company
- Opportunity to work on high-impact fintech problems at scale
- Build production-grade AI/ML systems with direct business ownership
- Work closely with leadership, product, and engineering teams
