I build stuff with data and AI. Been doing this for about 4 years now, mostly focused on turning messy data into things that actually help people make decisions.
I'm a Data Scientist who's really into GenAI, machine learning, and making analytics systems that people actually want to use. Not just building models for the sake of it – I care about whether they solve real problems.
GenAI & Agentic Systems (at OpenSky via CeADAR)
Built a Text-to-SQL system that doesn't completely break when someone asks a weird question. Used Azure OpenAI with some guardrails so it wouldn't generate nonsense queries. Cut SQL errors by 40%, which was pretty cool.
Made a regional similarity recommendation engine using kNN across 130+ regions. Analysts got their answers 35% faster, which means fewer angry Slack messages.
Put together a RAG platform for internal docs. No more "hey where's that document?" in every meeting. Saved about 30% of the time people spent hunting for information.
Built Power BI dashboards with natural language queries for a pharma client. They can now ask questions in plain English instead of learning DAX. Reporting got 30% more efficient.
Machine Learning & BI (at NextraIT)
Built customer retention models with Random Forest and XGBoost. Got a 15% boost in retention, which made the business folks happy.
Created executive dashboards that actually got used (shocking, I know). Decision-making got 20% faster.
Did the usual ML stuff – classification, anomaly detection, threshold optimization. Made sure to pick the right metrics instead of just chasing accuracy.
Cloud Analytics & Forecasting (at Invoice Fair)
Improved forecasting accuracy by 12% using ARIMA and SARIMAX. Played around with LSTM too, just to see.
Built 15+ Tableau dashboards for sales and customer success teams. Real-time updates, so they're not looking at yesterday's data.
Set up AWS pipelines with Glue, Athena, S3, and QuickSight. Data became 30% more accessible and we cut cloud costs by 10%. Win-win.
Right now I'm going deeper on AWS analytics (Glue, Athena, QuickSight, DynamoDB), Microsoft Fabric, and agentic AI systems. There's always something new to figure out.
GenAI Stuff: Text-to-SQL, RAG systems, prompt engineering, making sure LLMs don't go off the rails
Machine Learning: Time series forecasting, classification, anomaly detection, actually understanding what the metrics mean
BI & Dashboards: Power BI (including DAX and that AI Q&A feature), Tableau, designing KPIs that make sense
Cloud Platforms: AWS and Azure data tools, building pipelines that don't fall over
Finance & Risk: Fraud detection, credit risk modeling, portfolio stuff
Languages: Python, R, SQL
ML & Data: Pandas, NumPy, Scikit-learn, NLP libraries, statistical modeling
BI: Power BI, Tableau, Excel (yes, it still counts)
Cloud: AWS (Glue, Athena, S3, QuickSight), Azure (SQL, OpenAI, Synapse), Microsoft Fabric
GenAI: Azure OpenAI, LangChain, building RAG systems, API integrations
Want to talk about GenAI, ML projects, or just geek out about data? Hit me up:
📧 Email: [email protected]
💼 LinkedIn: linkedin.com/in/sharang75
P.S. If you're looking at my repos and wondering why some are messy – they're experiments. That's what GitHub is for, right?