Senior Data Scientist (UK)
- Contribute to the development of predictive and statistical models addressing business-critical challenges across diverse domains.
- Conduct exploratory data analysis, feature engineering, and hypothesis testing to uncover patterns and support model development.
- Collaborate with senior data scientists and ML engineers to refine models, improve accuracy, and enhance interpretability.
- Support the design and evaluation of experiments and A/B tests, ensuring rigorous measurement of impact.
- Clean, transform, and prepare data from diverse sources, ensuring high-quality datasets for analysis.
- Build dashboards, reports, and visualizations that communicate insights clearly to technical and non-technical stakeholders.
- Stay current with emerging data science methods and tools (e.g., generative AI, LLMs, causal inference) and apply them through prototyping.
- Contribute to the team's knowledge base by documenting workflows and sharing best practices.
- 5+ years of experience applying data science or advanced analytics in a professional setting.
- Solid understanding of statistical modeling, machine learning fundamentals, and experimental design.
- Experience with predictive modeling techniques such as regression, classification, clustering, or time-series forecasting.
- Proficiency in Python and experience with data science libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost, PyTorch, TensorFlow).
- Strong experience with SQL and data manipulation across large datasets.
- Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Tableau, or Power BI). Exposure to modern collaborative data platforms (e.g., Databricks, Snowflake, Palantir Foundry) is a plus.
- Excellent problem-solving skills, eagerness to learn, and ability to work in fast-paced, evolving environments.
- Strong written and verbal communication skills, with the ability to translate technical findings into business recommendations.
- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, or another quantitative discipline.
- Hands-on experience with Palantir platforms (Foundry, AIP, Ontology), including developing analytical workflows and deploying insights within enterprise environments.
- PhD in Data Science, Statistics, Computer Science, or a related quantitative field. Publications in top data science / ML conferences or journals (e.g., NeurIPS, ICML, KDD, ACL, or similar).
- Open-source contributions to the data science or ML community (libraries, notebooks, packages, or tutorials). Experience presenting research or applied work at meetups, workshops, or industry conferences.
- Familiarity with vector databases (FAISS, Pinecone, Milvus, Weaviate) and LLM application frameworks.
- Cloud or AI/ML certifications (e.g., AWS Machine Learning Specialty, Google Professional Data Engineer, Azure AI Engineer) are a plus.
- Work at the forefront of AI/ML innovation in life insurance, annuities, and financial services.
- Drive AI transformation for some of the most sophisticated financial entities.
- Competitive compensation, benefits, future equity options, and leadership opportunities.
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