Data Scientist

Responsibilities:
Understand business problems and structure analytical approaches to solve them.
Generate proof of concepts from data that help deliver improved business results.
Prepare and pre-process datasets needed to deliver analytical solutions.
Generate distinctive risk insights that deliver improved business results.
Build and implement predictive models and other analytical solutions using both structured and unstructured data.
Collaborate with Data Engineers, ML/AI Ops, and IT teams to deploy models into production environments and monitor model performance.
Support communication of findings, implications, and performance of the models to business stakeholders.
AI-related expectations:
Use AI-enabled tools and platforms, where available, across the data science lifecycle (for example, for data exploration, feature engineering, model experimentation, documentation support, and performance monitoring), while maintaining full accountability for the quality and robustness of models.
Identify opportunities to redesign analytical workflows using automation, AI agents and generative AI, with a focus on reducing manual work and accelerating experimentation and deployment.
Collaborate with stakeholders to ensure that AI and ML solutions include transparency, fairness, robustness and appropriate human oversight.
Requirements:
University education (Bachelor's degree or higher).
Ideal for fresh graduates or students finishing their degree.
Working knowledge of Python and basic data science packages.
Passion working with data and developing insights for use in business decisions.
Strong problem-solving and analytical skills from the following:
Advanced knowledge of statistical and predictive modelling techniques such as machine learning models (GBM, Random Forest, etc. ), decision trees, probability networks, association rules, clustering, regression, GLMs, SVMs, HMMs, time series, survival analysis, and neural networks and their application to business decisions.
Use state-of-the-art deep learning approaches (e. g., CNN, transformer, RNN/LSTM/GRU, GCN/GNN) to extract value from massive amounts of unstructured data (text, images).
Advanced knowledge of fine-tuning (e. g., PEFT, LoRA, RLHF) and customising large language models (e. g., GPT, BERT, Flan-T5 Llama, Dolly, BART), embedding approaches (e. g., Word2Vec, t-SNE), prompt engineering, and vectorisation.
Strong verbal and written communication skills, especially the ability to translate technical results for business audiences.
English - B2 or better.
Experience with Azure and Databricks is an advantage.
Experience with SQL and other programming languages is an advantage.
Prior work experience in insurance is a plus.
AI-related skills and mindset:
Experience or clear interest in using AI-supported tools (e. g., for data exploration, notebook assistance, code generation, experiment tracking or documentation) as part of day-to-day data science work.
Curious and proactive mindset towards learning new AI capabilities, experimenting safely, and sharing best practices that improve efficiency, insight generation and model performance across the team.

Experience
3-6 yrs