Senior Data Scientist

We seek a highly skilled and experienced Senior Data Scientist to join our team. In this role, you will lead advanced data science solutions' design, implementation, and optimisation. You will conduct complex experiments, uncover actionable insights, develop scalable machine-learning models, and drive strategic decisions based on data. As a Senior Data Scientist, you will mentor junior team members and collaborate across departments to elevate the organisation's data-driven capabilities. Main Goals and Responsibilities:Lead the development and deployment of robust data science and machine learning pipelines, from data retrieval and preprocessing to model training, inferencing, and results visualisation;Take ownership of the architecture and optimisation of DS/ML pipelines to ensure scalability and efficiency;Leverage ML models to build innovative solutions for tasks such as text generation, content automation, and more;Collaborate with cross-functional teams to integrate advanced data solutions that meet evolving business needs;Guide tools, strategies, and best practices to solve complex data science problems, including those involving LLMs and Generative AI;Share expertise with the team and across departments to elevate the overall technical capabilities of the organisation;Proactively identify opportunities to improve data processes, model performance, and business impact. What We Offer:Long-term career stability with a competitive salary paid in USD.Conditions for steady career development.Development supported by dedicated mentors and a variety of programs focused on expertise and innovation.Private medical insurance provided after successful completion of the probationary periodA well-equipped and cozy office supports comfort and productivity across all project stages.Welcoming atmosphere and a friendly corporate culture. Required Skills and Experience:English level Upper Intermediate +Strong mathematical and statistical background, including tensor calculusExpertise in databases such as Postgres, MongoDB, and SQLProficiency in Python, with extensive experience in libraries such as Scikit-learn, Numpy, Pandas, and MatplotlibIn-depth knowledge and experience with gradient boosting algorithms (e.g., XGBoost, LightGBM)Advanced experience with at least one neural network framework (TensorFlow/Keras, PyTorch), with a deep understanding of neural network architectures and optimisation techniquesProven commercial experience with classic machine learning and deep learning, including NLP and computer vision (e.g., BERT, ResNet)Experience in applying LLMs and Generative AI technologies (e.g., GPT, transformer-based architectures) in real-world use casesExtensive experience in building end-to-end machine learning pipelines (data ingestion, preprocessing, model training, deployment, inference) and managing CI/CD workflows using GitHub/GitLabPractical experience with Docker or Kubernetes platforms for deployment and scalabilityCommercial experience with cloud platforms like AWS or Azure Nice to Have:Familiarity with big data tools such as Spark and Airflow Required Skills and Experience:English level Upper Intermediate +Strong mathematical and statistical background, including tensor calculusExpertise in databases such as Postgres, MongoDB, and SQLProficiency in Python, with extensive experience in libraries such as Scikit-learn, Numpy, Pandas, and MatplotlibIn-depth knowledge and experience with gradient boosting algorithms (e.g., XGBoost, LightGBM)Advanced experience with at least one neural network framework (TensorFlow/Keras, PyTorch), with a deep understanding of neural network architectures and optimisation techniquesProven commercial experience with classic machine learning and deep learning, including NLP and computer vision (e.g., BERT, ResNet)Experience in applying LLMs and Generative AI technologies (e.g., GPT, transformer-based architectures) in real-world use casesExtensive experience in building end-to-end machine learning pipelines (data ingestion, preprocessing, model training, deployment, inference) and managing CI/CD workflows using GitHub/GitLabPractical experience with Docker or Kubernetes platforms for deployment and scalabilityCommercial experience with cloud platforms like AWS or Azure Nice to Have:Familiarity with big data tools such as Spark and Airflow