Open to opportunities

Atif User

@atif

Data scientist building ML systems, analytics pipelines, and LLM-powered applications that drive measurable business impact

India

Message

What I'm looking for

I seek a role where I can build and deploy ML systems end-to-end, work cross-functionally with product and stakeholders, and continue learning applied LLM and production ML techniques in a data-driven, collaborative environment.

I am a data scientist with experience building end-to-end machine learning systems, analytics pipelines, and production-ready AI applications that help businesses make better decisions from data.

At Arabania, I analyzed 100K+ sales records using advanced SQL and Python, built demand forecasting models that reduced food waste by 20%, and conducted pricing experiments that improved gross margins by 15%. I also developed predictive models for SKU performance and automated analytics workflows to improve operational efficiency.

My work focuses on real-world ML deployment, including churn prediction systems, real-time analytics dashboards, and energy market forecasting platforms. I have worked extensively with Python, SQL, scikit-learn, XGBoost, TensorFlow, FastAPI, and LLM integrations, with emphasis on interpretability, experimentation, and production-ready deployment.

I enjoy building practical AI systems that combine statistical rigor with real business impact.

Experience

Work history, roles, and key accomplishments

Data Analyst Intern

Arabania Indo-Arabic Restaurant

Jun 2024 - May 2025 (11 months)

Analyzed 100K+ sales records using complex SQL and Python to uncover seasonality and revenue trends, led A/B-style pricing experiments that improved gross margins by 15% and reduced food waste by 20% through demand forecasting.

SQL Python A B Testing Forecasting Power BI Hypothesis Testing SHAP

Data Scientist (Projects)

Independent / Personal Projects

Built end-to-end ML systems including a churn prediction platform, LLM-powered automation assistant, and real-time smart city analytics dashboard that improved recall for high-risk customers and reduced manual effort up to 80%.