EU remote
Senior Data Scientist with AI product mindset
About this role
Responsibilities 5 years of experience in different Data Science topics (e.g. machine learning, LLM, deep learning) Deliver independently Data Science projects, bring an e2e product-oriented mindset Engage directly with clients to understand their business objectives and translate them into technical requirements. Lead pre-sales technical discussions , demonstrating the value and capabilities of our analytics solutions.
Collaborate with cross-functional teams to integrate AI/ML solutions into the company's product offerings. Provide thought leadership and mentorship within the team , fostering a culture of continuous learning and innovation. Stay abreast of industry trends and advancements in AI/ML to ensure our solutions remain cutting-edge. General qualifications: Fluent English speaker with excellent communication skills, comfortable in client-facing roles.
5 years of experience in data science or a related field. Hands-on experience with LLM projects, machine learning, deep learning . Affinity to building product / product-oriented vision Domain expertise in any of the following fields is highly preferred: international finance, healthcare, pharma or meteorology. Technical requirements: Strong Python and SQL skills Leverage cloud-based data science tools on AWS (SageMaker, Bedrock), Azure, or GCP for scalable model training and deployment IDE (e.g.: Jupyter, VSCode) Version control Git / BitBucket / AZ DevOps Core machine learning libraries (sklearn, LightGBM, torch) Traditional ML projects (supervised and unsupervised) NLP experience / affinity: Understand concepts of text classification, information extraction Generative AI & LLM frameworks - HuggingFace / transformers, OpenAI; proprietary API & open-source models Understand concepts of RAG, build & maintain pipeline, understand concepts of pretraining, fine-tuning Nice to have: Databricks / distributed computing / scalable ML Familiarity with database systems (SQL, NoSQL) ML Lifecycle management (e.g.: MLFlow / wandb) Web frameworks (e.g.: Streamlit / FastAPI / Flask) Automatization pipelines on cloud (e.g.: Azure Functions, AWS Lambda, Databricks notebook jobs) API integration Experience with deploying ML models in production environments Experience with LangChain / LlamaIndex Understanding of LLM Agents, agentic behavior, prompt engineering Being able to differentiate what 'needs' LLMs and what can be solved with traditional ML / NLP Why us? Diverse projects: In each assignment there is always something new either on the technical or on the business side that helps you grow.