At a Glance
- Tasks: Lead the design and deployment of cutting-edge ML and GenAI solutions for automation.
- Company: Cognify Search, a forward-thinking tech company in London.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Join a dynamic team focused on innovation and scalability.
- Why this job: Shape the future of AI while making a real impact in fraud detection and risk management.
- Qualifications: Experience in ML engineering and strong collaboration skills are essential.
The predicted salary is between 80000 - 100000 £ per year.
Cognify Search in London is seeking a Lead ML Engineer to drive automation in claims, fraud detection and risk-based decisioning.
You will design, build and deploy production ML and Gen AI solutions, shaping architecture, tooling and processes to scale AI systems.
The role partners with Engineering and Product to deliver robust, production-ready solutions, influencing technical strategy and ensuring long-term scalability.
Strong collaboration and communication with stakeholders is essential.
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Senior ML & GenAI Lead for Production Systems 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 Senior ML & GenAI Lead for Production Systems
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We think you need these skills to ace Senior ML & GenAI Lead for Production Systems
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!
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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!
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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.
