At a Glance
- Tasks: Identify and prioritise AI opportunities, redesign processes, and prototype innovative solutions.
- Company: Join a leading firm at the forefront of AI and data science.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborate with diverse teams and drive innovation in a highly regulated industry.
- Why this job: Make a real impact by shaping the future of AI in a dynamic environment.
- Qualifications: Experience in Data Science/AI, Python, and stakeholder engagement required.
The predicted salary is between 80100 - 97900 £ per year.
- Identify, assess and prioritise AI and agentic workflow opportunities by value, feasibility, data readiness and risk.
- Redesign future-state processes across people, AI agents, data, systems, controls and human decision points.
- Design single- or multi-agent workflows using retrieval, tools/function calling, APIs, workflow state and escalation patterns.
- Prototype in Python across ML, NLP, Gen AI, RAG and agentic approaches; define measurable evaluation criteria.
- Partner with product, engineering, data, architecture and risk teams to move validated concepts towards production.
- Embed explainability, auditability, security, privacy, fairness, monitoring and human oversight throughout the lifecycle.
- SUCCESS MEASURES
- Prioritised pipeline of value-led, feasible AI use cases.
- Prototypes demonstrate measurable task, customer or operational outcomes.
- Solutions meet enterprise risk, governance and control expectations.
- Reusable patterns accelerate adoption across business and engineering workflows.
ESSENTIAL EXPERIENCE
- Strong applied Data Science/AI delivery from problem definition through implementation.
- Credible design or build experience with agents or multi-step agentic workflows.
- Hands-on Python, machine learning fundamentals, statistics, experimentation and model evaluation.
- Applied knowledge of foundation models, LLMs, RAG, embeddings, structured outputs and agent evaluation.
- Ability to translate complex business processes into AI-enabled operating models and clear delivery roadmaps.
- Experience integrating AI with enterprise data, APIs, applications and engineering workflows.
- Practical Responsible AI knowledge including guardrails, human oversight, explainability, bias and monitoring.
- Confident stakeholder engagement from senior business leaders to technical delivery teams.
- PREFERRED
- GCP, Vertex AI, Big Query and MLOps/LLMOps.
- Financial services or another highly regulated environment.
- Enterprise architecture, workflow orchestration or AI product/transformation experience.
DATA & AI SCIENTIST employer: PRACYVA
At PRACYVA, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation within the Lloyd’s Market. Our employees benefit from continuous professional development opportunities, competitive remuneration, and a supportive environment that values work-life balance, making it an ideal place for those looking to grow their careers in a vibrant London setting.
StudySmarter Expert Advice🤫
We think this is how you could land DATA & AI SCIENTIST
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like PRACYVA!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like DATA & AI SCIENTIST at PRACYVA.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like PRACYVA.
✨Apply Directly through Our Website
When you find a suitable opening like DATA & AI SCIENTIST at PRACYVA, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace DATA & AI SCIENTIST
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at PRACYVA, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at PRACYVA. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at PRACYVA
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at PRACYVA!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
