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As a Data Scientist at Hyperson, you help companies turn customer data into measurable business impact. After onboarding, you join one or more client projects where we build advanced analytics, predictive models, and Generative AI solutions that directly improve customer profitability and customer lifetime value.
You will work in hybrid, high-performing teams with data scientists, AI engineers, and strategy leads. You’ll master modern tools: Python, SQL, vector databases, cloud platforms, experiment frameworks, and the latest GenAI stack (LLMs, RAG, fine-tuning, agents, orchestration).
You will be coached by senior Hyperson leaders and given the space to develop fast. You’ll learn how to run full end-to-end processes, from data ingestion and modelling to deploying pipelines and validating real business uplift. You’ll also get exposure to clients, developing your consulting and communication skills in parallel with your technical expertise.
Most importantly: your work will be visible. You will see your models driving real decisions, from personalised marketing to churn prevention, pricing optimisation and next-generation GenAI assistants.
Over time, you can grow into various tracks: predictive modelling expert, generative AI engineer, analytics consultant, AI product owner, or even into commercial or leadership roles. At Hyperson, your career evolves as fast as AI does.
You love diving into large datasets and discovering value others haven’t seen yet. You enjoy testing hypotheses, finding patterns, building models, and turning complexity into clarity.
You translate customer behaviour into predictions, segments, actions, and measurable outcomes. You design experiments, track impact, and iterate continuously.
You collaborate closely with marketers, product teams, and business stakeholders, helping them understand what the data says and what they should do next. You think in CLV, uplift, customer equity and business impact, not in isolated metrics or dashboards.
You combine classical data science with the newest innovations in Generative AI: retrieval-augmented generation, agentic workflows, domain-specific LLMs, and automated analytics. Your goal is always the same: deliver insight, drive action, and improve customer value.