We are seeking an experienced Data Scientist with a passion for large language models (LLMs) and cutting-edge AI research. In this role, you will design and prototype data preparation pipelines, collaborating closely with data engineers to transform your prototypes into scalable production pipelines, design and implement a state-of-the-art evaluation and benchmarking framework to measure and guide model quality, and do end-to-end LLMs training. You will work alongside top AI researchers and engineers, ensuring our models are not only powerful but also aligned with user needs, cultural context, and ethical standards.
About us
Kyivstar.Tech is a Ukrainian hybrid IT company and a resident of Diia.City.We are a subsidiary of Kyivstar, one of Ukraine’s largest telecom operators.
Our mission is to change lives in Ukraine and around the world by creating technological solutions and products that unleash the potential of businesses and meet users’ needs.
Over 500+ KS.Tech specialists work daily in various areas: mobile and web solutions, as well as design, development, support, and technical maintenance of high-performance systems and services.
We believe in innovations that truly bring quality changes and constantly challenge conventional approaches and solutions. Each of us is an adherent of entrepreneurial culture, which allows us never to stop, to evolve, and to create something new.
What you will do * Design, prototype, and validate data preparation and transformation steps for LLM training datasets, including cleaning and normalization of text, filtering of toxic content, de-duplication, de-noising, detection and deletion of personal data, etc. * Form specific SFT/RLHF datasets from existing data, including data augmentation/labeling with LLM as teacher. * Develop heuristics, filtering rules, adversarial examples, and synthetic data generation methods to maximize model robustness and data effectiveness. * Research and develop best practices and novel techniques in LLM training pipelines. * Develop metrics, tools and processes for continuous evaluation during model pre-training, fine-tuning, and deployment. * Analyze benchmarking datasets, define gaps and design, implement, and maintain comprehensive benchmarking framework for Ukrainian language. * Monitor and analyze the impact of data quality and benchmark results on model performance, identifying strengths, weaknesses, and improvement opportunities. * Collaborate closely with data engineers, annotators, linguists, and domain experts to scale data processes, define evaluation tasks and collect high-quality feedback. * Document methodologies, experimental findings, and best practices, and share insights across internal teams to ensure alignment of training and evaluation workflows.
Qualifications and experience needed
Education & Experience: * 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP. * Proven experience in end-to-end ML, NLP model development, including data preparation and evaluation. * An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.
NLP Expertise: * Good knowledge of natural language processing techniques and algorithms. * Hands-on experience with modern NLP approaches, including embedding models, semantic search, text classification, sequence tagging (NER), transformers/LLMs, RAGs. * Familiarity with LLM training and fine-tuning techniques.
ML & Programming Skills: * Proficiency in Python and common data science and NLP libraries (pandas, NumPy, scikit-learn, spaCy, NLTK, langdetect, fasttext). * 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. * 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, Ray, Dask) for scaling data processing or model training is a plus.
Communication: * Experience working in a collaborative, cross-functional environment. * Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies clearly. * Ability to rapidly prototype and iterate on ideas
A plus would be
Advanced NLP/ML Techniques: * Solid understanding of RLHF concepts and related techniques (preference modeling, reward modeling, reinforcement learning). * Prior work on LLM safety, fairness, and bias mitigation. * Ability to rapidly prototype and iterate on ideas * Familiarity with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency. * Knowledge of data annotation workflows and human feedback collection methods.
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) indicates 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. * Knowledge of Ukrainian text sources and data sets, or experience with multilingual data processing, can be an advantage given our project’s focus. * Knowledge of Ukrainian benchmarks, or familiarity with other evaluation datasets and leaderboards for large models, can be an advantage given our project’s focus.
MLOps & Infrastructure: * Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow). * 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.
What we offer * Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace * Remote onboarding * Performance bonuses * We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners * Health and life insurance * Wellbeing program and corporate psychologist * Reimbursement of expenses for Kyivstar mobile communication
