We are looking for a Mid-to-Senior Data Scientist who can deliver end-to-end solutions, communicate both technical and business aspects to clients, and take ownership of production outcomes — helping Dastellar fulfill its mission to build custom AI/ML/Data Science solutions for businesses.

General Requirements

• Experience: 5+ years in ML/Data Science.
• LLMs / GenAI: hands-on experience deploying and fine-tuning LLMs (e.g., GPT/LLaMA/Hugging Face), building RAG/embedding pipelines and using LangChain or equivalents.
• Classical ML: proven work with sklearn, XGBoost/LightGBM, time-series methods (ARIMA/Prophet/ETS or deep models), recommendation systems.
• Python: daily use of Python 3.8+, solid Pandas/NumPy; experience with PyTorch or TensorFlow in at least one production project.
• Production: experience deploying models (Docker, CI/CD), setting up monitoring; basic cloud skills (AWS/GCP/Azure) preferred.
• Communication: experience presenting technical topics to clients and translating technical risks into business impact.
• Autonomy: history of leading end-to-end tasks without continuous supervision; ready to provide examples of decisions owned.
• Language: English — B2+.

Would be a plus

• Experience with vector DBs (FAISS, Qdrant, Pinecone).
• MLOps basics: experiment tracking (MLflow, W&B), CI/CD pipelines, automation tools.
• Big Data experience (Spark) and advanced SQL optimization.
• Deep domain knowledge in energy or retail.

Your main responsibilities are

• Design and deliver custom ML/GenAI solutions (POC to production).
• Build models: LLMs (fine-tuning, prompt engineering), embeddings, RAG; classic ML (classification, regression, time-series, clustering, recommender systems).
• Build ETL/ELT pipelines, feature engineering, data cleaning and validation.
• Design solution architecture and select the stack (deployment infra, vector DBs, orchestration).
• Deploy models and integrate with infra (Docker, CI/CD, optionally Kubernetes or serverless).
• Set up model and system monitoring (model metrics, data drift, alerts).
• Prepare technical documentation, proposals and client presentations.
• Conduct research and technical experiments; document R&D results.
• Perform code reviews, write tests and ensure code/model quality.

Soft Skills

• Responsibility: Met deadlines, maintained post-release SLAs and can provide project timelines with milestone outcomes as evidence.
• Proactivity: Proposed and implemented multiple processes or architecture improvements with measurable impact (reduced time-to-delivery or error rate), documented with a short case note describing outcome.
• Autonomy: Delivered milestones on multi-month projects with only weekly or biweekly syncs (no day-to-day supervision); able to produce independent milestone plans and status reports.
• Ownership: Owned multiple production ML services end-to-end - architecture, deployment, monitoring and incident resolution. Can present a 1-page case study showing decisions and results.
• Problem solving: Translated ambiguous business goals into a 2–4 sprint MVP roadmap with prioritized tasks, acceptance criteria and success metrics; provide an example roadmap or case.
• Critical thinking: Identified data/assumption biases or failure modes and proposed validation/mitigation tests.

What we offer

• Flexible part-time / contract work (compatible with other projects).
• Real client cases in energy and e-commerce.
• Fast decision-making and low bureaucracy.
• Opportunity to influence architecture and stack choices.
• Remote collaboration and flexible schedule.
• Paid R&D participation (by agreement).