Data Scientist, zaizi

• Action-oriented and eager to take ownership of projects and initiative
• Start-up mindset
• A problem solver with the ability to be client facing
• Ability to switch from exploratory code to production-ready code
• Passion for monitoring and observability of deployed solutions
• Proactive and collaborative approach to problem-solving
• Ability to effectively communicate technical concepts
• Broad knowledge of various Machine Learning techniques
• Understanding of when simpler solutions are more effective
• Team player who loves working within a multidisciplinary team
• Strong proficiency in Python and Data Science libraries like scikit-learn or equivalent
• Experience with SQL/Databricks for data exploration
• Ability to explain data science concepts and solutions to broad audiences
• Proven record of deploying models to production, ideally as serverless applications
• Security Clearance: Certain projects demand that our staff be British and cleared to SC level (or eligible for clearance)

Desirable

• As a Data Scientist at Zaizi, you will be responsible for analysing data to find and bring value to our clients
• This involves a mixture of business acumen and technical expertise in data science disciplines
• You will be digging through data to deliver data-driven, machine learning solutions
• Take ownership of projects and initiatives
• Deliver at pace and have a "get things done" attitude
• Develop scrappy exploratory code and code that adheres to best practices and production-ready standards
• Design and implement workflows
• Optimising data pipelines to improve clients performance and scalability whilst maintaining their data integrity
• Monitor and ensure observability of deployed solutions
• Collaborate with technical and non-technical team members
• Apply various machine learning techniques, understanding their benefits and drawbacks
• Supporting Zaizi in response to ML bid requirements
• Choose the appropriate machine learning technique for the task at hand
• Understand when simpler solutions are more effective than complex ones
• Work within a multidisciplinary team and collaborate with other teams
• Implement and follow ML Ops best practices with deep frameworks
• Deploy models to production, ideally as serverless applications
• Understand and implement data protection and security practices
• Familiarity with cloud platforms
• Familiarity with CI/CD pipelines and cloud-based deployment

• Interview Process:
• 1st stage:
• Initial phone screening done by a member of the recruitment team
• 2nd stage:
• Technical interview, this stage evaluates your technical expertise and problem-solving abilities, especially if you're applying for a technical role
• Final Stage:
• 10-15 min presentation and a panel interview where multiple people from different departments ask you questions