🧠 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 disease detection with Fast R-CNN
• A neural-network chess move predictor
What I enjoy most is shaping messy data, choosing the right models, and evaluating solutions realistically.
👉 If you’re building NLP or computer vision products and need someone hands-on, I’d love to connect.
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👋 Hi Contra — I’m Lilya, a Data Scientist working at the intersection of retail, demand forecasting, and promotion analytics.
My recent work focuses on SKU-level forecasting, where small modeling choices have a big business impact:
• 📦 Improving SKU-level demand forecasts for planning & inventory
• 📊 Approximating the pull-forward effect of promotions
On the technical side, I work end-to-end:
• Time-series & ML forecasting
• Promo, price & calendar feature engineering
• PySpark batch pipelines & MLflow tracking
💡 Open to freelance or contract projects around demand planning, retail analytics.
Happy to connect 🚀
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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 & evaluation (time-series & ML)
• 🚀 Production pipelines & deployment
On the engineering side, I focus on making ML reliable and scalable:
• PySpark pipelines for feature engineering, batch training & inference
• MLflow for experiment tracking, model versioning & reproducibility
I enjoy working with teams that care about clean ML systems, not just experiments , especially when models need to be trusted and reused.
👉 If you’re looking for someone to build ML pipelines end to end , from data exploration to production
I’m open to contract or freelance work.
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🤖 Hi another side of my work is building LLM-powered systems that actually ship.
I’ve worked on chatbots and AI assistants from research to production, focusing on usefulness and reliability:
• Designing LLM-based recommenders using LangChain & FAISS
• Fine-tuning transformer models (BERT, T5 / FLAN-T5) for real NLP tasks
• Testing, debugging, and improving chatbot behavior based on user feedback
⚙️ Tech I often use: PyTorch, Hugging Face, LangChain, vector search, MLflow
💡 Open to freelance or contract work where teams need:
• LLM integration or fine-tuning
• Chatbots or AI assistants that deliver real value
• Practical NLP solutions, not demos
Happy to connect with teams building AI-driven products 🚀
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