Staff Data Scientist (UK)
- Lead the design and execution of data science projects that solve complex business problems across critical workflows.
- Develop and apply advanced AI/ML methods including statistical modeling, causal inference, forecasting, optimization, and machine learning.
- Partner with stakeholders to translate business challenges into analytical frameworks, ensuring results are actionable and aligned with strategic priorities.
- Drive the adoption of emerging analytical techniques and tools (e.g., generative AI, LLM-based analytics, simulation modeling, RAG for knowledge discovery).
- Collaborate with ML engineers to scale prototypes into production-ready systems, ensuring reliability, fairness, and generalizability.
- Design and maintain metrics and experiments (A/B testing, uplift modeling, KPI design) to measure model and business impact.
- Communicate findings through compelling data storytelling, dashboards, and executive-level presentations.
- Provide thought leadership in responsible AI practices, ensuring transparency, fairness, and compliance with internal governance and external regulations.
- Mentor and guide other data scientists, fostering technical excellence, innovation, and collaboration across the team.
- 8+ years of experience in data science or applied statistics roles, with a proven track record of driving measurable business impact.
- Expertise in one or more of supervised and unsupervised machine learning, deep learning, time series analysis, causal inference, and statistical modeling.
- Experience leading end-to-end data science projects from ideation to delivery, including business scoping and stakeholder management.
- Strong proficiency in Python (or R), with deep experience using modern data science libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Statsmodels).
- Solid foundation in SQL and data wrangling across large, complex datasets.
- Hands-on experience with experimentation platforms, data visualization, and dashboarding tools (e.g., Tableau, Power BI, Plotly).
- Familiarity with cloud-based data platforms (AWS, GCP, Azure) and collaborative tools (Databricks, Snowflake) is a plus.
- Exceptional ability to translate technical results into clear, actionable insights for senior executives and non-technical audiences.
- Master's or PhD in Statistics, Data Science, Computer Science, Economics, or a closely related discipline.
- Experience working with Palantir platforms (Foundry, AIP, Ontology) to develop, analyze, and operationalize data-driven insights within enterprise-scale environments.
- PhD in Data Science, Statistics, Computer Science, or a related quantitative discipline. Publications in top-tier AI/ML or data science conferences or journals (e.g., NeurIPS, ICML, KDD, AAAI, ACL, JASA).
- Recognized contributions to the open-source data science / ML ecosystem (e.g., libraries, frameworks, toolkits, widely adopted notebooks).
- Track record of thought leadership through invited talks, keynote presentations, or leadership roles in professional societies, conferences, or meetups.
- Experience mentoring teams at scale and establishing standards for reproducibility, experimentation, and responsible AI.
- Familiarity with vector databases, knowledge graphs, and LLM application frameworks for advanced analytics.
- Cloud or AI/ML certifications (e.g., AWS ML Specialty, Google Cloud ML 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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