“You don’t need a full ML team”
Many teams don’t need a full ML department.
They need:
• one clear use case
• clean data pipelines
• a reliable first model
I help teams get there efficiently, without overbuilding.
Open to short-term or part-time collaborations.
🧠 Need a data scientist who understands both models and product?
I work with product and engineering teams to turn machine learning into features users actually benefit from.
My background includes:
• Improving search and discovery for e-commerce platforms
• Building chatbots...
I help teams turn messy business data into production-ready machine learning solutions.
My experience spans the full ML lifecycle:
• Exploratory data analysis & feature engineering (Pandas & PySpark)
• Training and evaluating ML models
• Building scalable data pipelines
•...
🧠 Hi Contra, alongside retail forecasting, I’ve worked on applied NLP and computer vision projects that go beyond experiments and turn into real solutions.
I’ve built:
• Review rating prediction with BERT
• Text summarization using FLAN-T5
• Sign language classification
• Fruit...
Hi Contra , a big part of my work is building ML projects end to end, from exploration to deployment.
I work across the full lifecycle:
• 🔍 EDA & data validation on large, messy datasets
• 🧩 Feature engineering with PySpark driven by business logic
• 🧠 Model training &...
