Senior Data Scientist/NLP Lead

Kyivstar.Tech is seeking an experienced Senior Data Scientist / NLP Lead to spearhead the development of cutting-edge natural language processing solutions for our Ukrainian LLM project. You will lead our NLP team in designing, implementing, and deploying large-scale language models and NLP algorithms that power our products. This role is critical to our 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.

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 

• 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 our 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 the model development process.

Qualifications and experience needed 

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.

• 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 in building 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.

A plus would be 

LLM training & evaluation experience:

• Experience with tokenizer development, SFT, and RLHF techniques.

• Knowledge of model safety: toxicity, hallucinations, ethical considerations, and 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 cultural context for model training and evaluation.

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:

• Creative mindset for tackling open-ended AI challenges.

• Comfort in fast-paced R&D environments with evolving priorities.

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.