At a Glance
- Tasks: Lead ML initiatives in claims automation, fraud detection, and risk-based decisioning.
- Company: Join a forward-thinking company focused on AI and machine learning solutions.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Exciting role with potential for significant impact in regulated environments.
- Why this job: Shape the future of AI while influencing technical strategy and scalability.
- Qualifications: Strong experience in ML solutions and excellent communication skills required.
The predicted salary is between 80000 - 100000 £ per year.
The role:
- Lead ML initiatives across claims automation, fraud detection and risk-based decisioning
- Design, build and deploy production ML and GenAI solutions
- Shape the architecture, tooling and processes required to scale AI systems
- Partner with Engineering and Product teams to deliver robust, production-ready solutions
- Influence technical strategy and challenge approaches to ensure long-term scalability
What we’re looking for:
- Strong commercial experience delivering end-to-end ML solutions from experimentation through to production
- Strong statistical modelling and machine learning background
- Experience applying ML within risk, fraud, insurance, finance or other regulated environments
- Hands-on experience building and deploying Generative AI/LLM solutions in production (or strong practical exposure)
- Ability to think at a systems level across data, models and engineering
- Excellent communication skills, with confidence influencing senior stakeholders
Principal Data Scientist employer: Cognify Search
Cognify Search offers an exceptional work environment for the Head of ML & AI, where innovation meets collaboration. With a focus on enhancing customer experience through cutting-edge technology, employees benefit from a culture that prioritises professional growth and teamwork. Located in a dynamic eCommerce hub, this role provides unique opportunities to lead impactful projects while working alongside industry experts.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Data 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 Cognify Search!
✨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 Principal Data Scientist at Cognify Search.
✨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 Cognify Search.
✨Apply Directly through Our Website
When you find a suitable opening like Principal Data Scientist at Cognify Search, 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 Principal Data 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 Cognify Search, 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 Cognify Search. 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 Cognify Search
✨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 Cognify Search!
✨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.
