Middle Data Scientist (NLP)

Описание

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 focus on designing and prototyping data preparation pipelines, collaborating closely with data engineers to transform your prototypes into scalable production pipelines and actively develop model training pipelines with other talented data scientists. Your work will directly shape the quality and capabilities of our models by ensuring we feed them the highest-quality, most relevant data possible. The datasets you build directly determine model capability, safety, and cost, raising downstream task accuracy, reducing training waste, and accelerating time-to-market for product teams.About us is a Ukrainian hybrid IT company and a resident of 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+ 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.• Formation of specific SFT/RLHF datasets from existing data, including data augmentation/labeling with LLM as teacher.• Analyze large-scale raw text, code, and multimodal data sources for quality, coverage, and relevance.• Develop heuristics, filtering rules, and cleaning techniques to maximize training data effectiveness.• Collaborate with data engineers to hand over prototypes for automation and scaling.• Research and develop best practices and novel techniques in LLM training pipelines.• Monitor and evaluate data quality impact on model performance through experiments and benchmarks.• Research and implement best practices in large-scale dataset creation for AI/ML models.• Document methodologies and share insights with internal teams.Qualifications and experience neededEducation & Experience: • 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP.• Proven experience in data preprocessing, cleaning, and feature engineering for large-scale datasets of unstructured data (text, code, documents, etc.). • 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, and data requirements.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 (, 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 & Personality:• Experience working in a collaborative, cross-functional environment.• Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies.• Ability to rapidly prototype and iterate on ideasA plus would beAdvanced NLP/ML Techniques: • Familiarity with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.• Understanding of FineWeb2 or a similar processing pipeline approachResearch & Community: • Publications in NLP/ML conferences or contributions to open-source NLP projects. • Active participation in the AI community or demonstrated continuous learning (, Kaggle competitions, research collaborations).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.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.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.