Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
About the client: Our client is an IT company that develops technological solutions and products to help companies reach their full potential and meet the needs of their users. The team comprises over 600 specialists in IT and Digital, with solid expertise in various technology stacks necessary for creating complex solutions.
About the role: We are looking for an experienced Senior Data Scientist / NLP Lead to spearhead the development of cutting-edge natural language processing solutions for the Ukrainian LLM project. You will lead the NLP team in designing, implementing, and deploying large-scale language models and NLP algorithms that power the products.
This role is critical to the mission of advancing AI in the Ukrainian language context, and offers the opportunity to drive technical decisions, mentor a team of data scientists, and shape the future of AI capabilities in Ukraine.
Requirements: Education & Experience: — 5+ years of experience in data science or machine learning, with a strong focus on NLP. — Proven track record of developing and deploying NLP or ML models at scale in production environments. — An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred. NLP Expertise: — Deep understanding of natural language processing techniques and algorithms. — Hands-on experience with modern NLP approaches, including embedding models, text classification, sequence tagging (NER), and transformers/LLMs. — Deep understanding of transformer architectures and knowledge of LLM training and fine-tuning techniques, hands-on experience developing solutions on LLM, and knowledge of linguistic nuances in Ukrainian or other languages. Advanced NLP/ML Techniques: — Experience with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency. — Background in information retrieval or RAG (Retrieval-Augmented Generation) is a plus for building systems that augment LLMs with external knowledge. ML & Programming Skills: — Proficiency in Python and common data science libraries (pandas, NumPy, scikit-learn). — Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models. — Ability to write efficient, clean code and debug complex model issues. Data & Analytics: — Solid understanding of data analytics and statistics. — Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance. — Experience on how to build a representative benchmarking framework given business requirements for LLM. — Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions. Deployment & Tools: — Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications. — Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML). — Experience with cloud platforms (AWS, GCP or Azure) and big data technologies (Spark, Hadoop) for scaling data processing or model training is a plus. — Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow). Leadership & Communication: — Demonstrated ability to lead technical projects and mentor junior team members. — Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies clearly.
Nice to have: LLM training & evaluation experience: — Hands-on experience of building tokenizers and SFT, RLHF techniques. — Knowledge of the evaluation of model toxicity and ethical aspects, hallucinations, security, and building LLM guardrails. Research & Community: — Publications in NLP/ML conferences or contributions to open-source NLP projects. — Active participation in the AI community or demonstrated continuous learning (e.g., Kaggle competitions, research collaborations) indicating a passion for staying at the forefront of the field. Domain & Language Knowledge: — Familiarity with the Ukrainian language and context. — Understanding of cultural and linguistic nuances that could inform model training and evaluation in a Ukrainian context. MLOps & Infrastructure: — Experience in working alongside MLOps engineers to streamline the deployment and monitoring of NLP models. Problem-Solving: — Innovative mindset with the ability to approach open-ended AI problems creatively. — Comfort in a fast-paced R&D environment where you can adapt to new challenges, propose solutions, and drive them to implementation.
Responsibilities: — Lead end-to-end development of NLP and LLM models — from data exploration and model prototyping to validation and production deployment. This includes designing novel model architectures or fine-tuning state-of-the-art transformer models (e.g., BERT, GPT) to solve project-specific language tasks. — Analyze large text datasets (Ukrainian and multilingual corpora) to extract insights and build robust training datasets. — Guide data collection and annotation efforts to ensure high-quality data for model training. — Develop and implement NLP algorithms for a range of tasks such as text classification, named entity recognition, semantic search, and conversational AI. — Stay up-to-date with the latest research to apply transformer-based models, embeddings, and other modern NLP techniques in the solutions. — Establish evaluation metrics and validation frameworks for model performance, including accuracy, factuality, and bias. — Design A/B tests and statistical experiments to compare model variants and validate improvements. — Deploy and integrate NLP models into production systems in collaboration with engineers — ensuring models are scalable, efficient, and well-monitored in a real-world setting. — Optimize model inference and troubleshoot issues such as model drift or data pipeline bottlenecks. — Provide technical leadership and mentorship to the NLP/ML team. — Review code and research, uphold best practices in ML (version control, reproducibility, documentation), and foster a culture of continuous learning and innovation. — Collaborate cross-functionally with product managers, software engineers, and MLOps engineers to align NLP solutions with product goals and infrastructure capabilities. — Communicate complex data science concepts to stakeholders and incorporate their feedback into model development.
The company offers: — Competitive salary. — Equity options in a fast-growing AI company. — Remote-friendly work culture. — Opportunity to shape a product at the intersection of AI and human productivity. — Work with a passionate, senior team building cutting-edge tech for real-world business use.
