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/Middle Data Scientist with a passion for Large Language Models (LLMs) and cutting-edge AI research. In this role, you will design and implement a state-of-the-art evaluation and benchmarking framework to measure and guide model quality, and personally train LLMs with a strong focus on Reinforcement Learning from Human Feedback (RLHF). You will work alongside top AI researchers and engineers, ensuring the models are not only powerful but also aligned with user needs, cultural context, and ethical standards.
Requirements: Education & Experience: — 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP. — Proven experience in machine learning model evaluation and/or NLP benchmarking. — 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. — Solid understanding of RLHF concepts and related techniques (preference modeling, reward modeling, reinforcement learning). — Ability to write efficient, clean code and debug complex model issues. Data & Analytics: — Solid understanding of data analytics and statistics. — Experience creating and managing test datasets, including annotation and labeling processes. — 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. 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.
Nice to have: Advanced NLP/ML Techniques: — Prior work on LLM safety, fairness, and bias mitigation. — 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) 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. — Knowledge of Ukrainian benchmarks, or familiarity with other evaluation datasets and leaderboards for large models, can be an advantage given the 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.
Responsibilities: — Analyze benchmarking datasets, define gaps, and design, implement, and maintain a comprehensive benchmarking framework for the Ukrainian language. — Research and integrate state-of-the-art evaluation metrics for factual accuracy, reasoning, language fluency, safety, and alignment. — Design and maintain testing frameworks to detect hallucinations, biases, and other failure modes in LLM outputs. — Develop pipelines for synthetic data generation and adversarial example creation to challenge the model’s robustness. — Collaborate with human annotators, linguists, and domain experts to define evaluation tasks and collect high-quality feedback — Develop tools and processes for continuous evaluation during model pre-training, fine-tuning, and deployment. — Research and develop best practices and novel techniques in LLM training pipelines. — Analyze benchmarking results to identify model strengths, weaknesses, and improvement opportunities. — Work closely with other data scientists to align training and evaluation pipelines. — Document methodologies and share insights with internal teams.
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.
