Senior Data Scientist with AI product mindset

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

Employment type
Full-time

Work arrangement
Hybrid