RJ.

Ranveer

Data Science & Applied Machine Learning

I design and deploy data-driven solutions—from time‑series modeling and NLP to risk analytics—that turn complex datasets into business decisions. This page intentionally avoids personal contact details; use the button above to request them securely.

Finance Insurance Telecom Tech Consulting
  • Focus
    ML Systems, Quant Analytics, Risk
  • Toolkit
    Python, SQL, PyTorch, scikit‑learn
  • Cloud
    AWS, Azure, GCP
  • Data
    Pipelines, MLOps, Experimentation

About

Applied ML engineer and quantitative problem‑solver with experience across highly regulated industries. I partner with cross‑functional teams to frame problems, ship models, and measure impact—balancing statistical rigor with production constraints.

This site highlights representative work and skills while deliberately omitting phone, email, home address, and other personal identifiers. If you need those, use the secure request flow.

Core Skills

Machine Learning

  • Supervised & unsupervised modeling
  • Time‑series forecasting & anomaly detection
  • NLP: classification, topic modeling, embeddings
  • Model monitoring & drift management

Engineering

  • Data pipelines (batch & streaming)
  • APIs, microservices, orchestration
  • Containers & CI/CD for ML (MLOps)
  • Cloud: AWS / Azure / GCP

Quant & Analytics

  • Risk & portfolio analytics
  • Statistical inference & experimentation
  • Data visualization & storytelling
  • Regulatory & model governance awareness
Python SQL PyTorch scikit‑learn Pandas Airflow Spark Docker Kubernetes

Experience Highlights

Risk & Quant Analytics

Developed and validated models for risk scoring and forecasting; partnered with stakeholders to translate model outputs into decisions. Reduced false positives and improved stability across market regimes.

Applied ML in Production

Shipped NLP and time‑series services behind APIs with monitoring and A/B testing, cutting model latency and improving downstream KPIs.

Cross‑Industry Impact

Delivered data products across finance, insurance, telecom, and tech—balancing compliance, model governance, and user needs.

UBS Swiss Re Credit Suisse Apple Swisscom EY Deloitte BCG Allianz Sony

(Listed for context only; this section intentionally omits dates, locations, and contact details.)

Selected Projects

Market Risk Forecasting Pipeline

End‑to‑end time‑series pipeline (feature store → model registry → monitoring) that improved forecast MAPE and reduced incident load.

  • Stack: Python, Airflow, MLflow, Kubeflow
  • Impact: Higher accuracy & faster retrains

NLP for Document Intelligence

Built classification & extraction on unstructured text with embeddings and weak supervision; exposed via REST API.

  • Stack: PyTorch, Transformers, FastAPI
  • Impact: Triage time reduced

Model Governance Toolkit

Templates & checks for documentation, fairness, and drift to streamline approvals in regulated environments.

  • Stack: Great Expectations, Evidently, CI/CD
  • Impact: Faster audit cycles

Insights

Measuring Model Value Beyond Accuracy

Why cost curves, calibration, and decision metrics beat leaderboard chasing—especially in production systems.

A Practical View of MLOps

Lightweight patterns to move from notebooks to stable services without over‑engineering.

Privacy Notice

This site intentionally excludes email, phone number, physical address, and personal identifiers. Any request for contact details is handled via a consent‑based flow. Analytics are disabled; no cookies are set.

Connect on LinkedIn

To request my resume or contact details, please connect with me directly on LinkedIn.

Go to LinkedIn