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- Tasks: Lead client projects and solve complex data problems using innovative data science techniques.
- Company: Join a dynamic, agile organization that values transparency and collaboration.
- Benefits: Enjoy professional growth, a hybrid work model, and a respectful work environment.
- Why this job: Make a real impact with your skills while working in a culture that encourages daring innovation.
- Qualifications: Experience in data science, machine learning, and cloud infrastructure is essential.
- Other info: If you're passionate about technology and problem-solving, apply now!
The predicted salary is between 43200 - 72000 £ per year.
This permanent position offers the opportunity to work on complex data problems and deliver innovative solutions that drive real impact. Key Responsibilities: Leading client projects and providing subject matter expertise. Working in scrum-like environments for iterative and 'fail-fast' work and innovation. Assessing clients' business and technical needs to identify opportunities for data science usage. Solving problems using data science techniques and in a scientifically robust fashion. Identifying relevant data sources and leveraging them to meet client needs. Modelling various forms of data for efficient data science use. Investigating and analysing data to uncover meaningful insights. Applying statistical and evidence-based techniques to inform insights and actions. Communicating technical content appropriately both internally and to customers. Building maintainable code using existing or novel data science techniques. Designing, evaluating, and implementing data science and machine learning techniques. Developing scalable models and algorithms for deployment in production environments. Applying ethical principles in handling data. Delivering high-quality work to agreed timelines and taking the initiative. Supporting client engagements, including pitches and presentations. Contributing to the company strategy and helping to shape the future. Job Requirements: Experience in data science, machine learning algorithms, and data engineering. Industry experience in consultancy, engineering, or data science. Significant experience with cloud-based infrastructure (e.g., AWS, Azure, GCP). Proficiency in Python and relevant data science libraries. Experience in using CI/CD tooling for code deployment and testing. Knowledge of database technologies (e.g., SQL, NoSQL such as Elasticsearch and Graph databases). Understanding of coding best practices, design patterns, and versioning. Strong interpersonal skills and the ability to communicate effectively with clients and colleagues. Benefits: Joining a dynamic and agile organisation. Opportunities for professional growth and development. Working in an environment that values transparency, fairness, and daring. Collaborative and respectful work environment. Hybrid working model with 2-3 days in the office or on a client site. If you are a skilled Data Scientist with the necessary clearance and a passion for technology and problem-solving, we would love to hear from you. Apply now to join our client's innovative and forward-thinking team
Data Scientist eDV employer: Matchtech
Contact Detail:
Matchtech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist eDV
✨Tip Number 1
Familiarize yourself with the specific data science techniques and machine learning algorithms mentioned in the job description. Being able to discuss these in detail during your interview will show that you have the expertise needed for the role.
✨Tip Number 2
Highlight any experience you have with cloud-based infrastructure like AWS, Azure, or GCP. Be prepared to share examples of how you've utilized these platforms in past projects to solve complex data problems.
✨Tip Number 3
Since the role involves client engagement, practice articulating technical concepts in a way that's easy for non-technical stakeholders to understand. This will demonstrate your strong interpersonal skills and ability to communicate effectively.
✨Tip Number 4
Showcase your coding skills by discussing your experience with Python and relevant libraries. If possible, prepare to present a project where you applied coding best practices and CI/CD tooling for deployment.
We think you need these skills to ace Data Scientist eDV
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data science, machine learning algorithms, and consultancy. Use specific examples that demonstrate your ability to solve complex data problems and deliver innovative solutions.
Craft a Compelling Cover Letter: In your cover letter, express your passion for technology and problem-solving. Mention your experience with cloud-based infrastructure and proficiency in Python, and explain how these skills align with the company's needs.
Showcase Relevant Projects: Include details about past projects where you applied data science techniques. Highlight your role in leading client projects, using statistical methods, and developing scalable models, as these are key responsibilities of the position.
Prepare for Technical Questions: Be ready to discuss your technical expertise during the interview process. Familiarize yourself with coding best practices, CI/CD tooling, and database technologies, as these will likely come up in discussions.
How to prepare for a job interview at Matchtech
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific data problems you've solved in the past. Highlight your approach, the techniques you used, and the impact of your solutions. This will demonstrate your ability to tackle complex challenges effectively.
✨Familiarize Yourself with Agile Methodologies
Since the role involves working in scrum-like environments, it’s crucial to understand agile principles. Be ready to discuss how you've applied these methodologies in previous projects and how they can enhance collaboration and innovation.
✨Communicate Technical Concepts Clearly
You’ll need to explain technical content to both clients and colleagues. Practice simplifying complex ideas and using analogies to make your points clear. This skill is essential for effective client engagement and teamwork.
✨Demonstrate Ethical Data Handling
Given the importance of ethical principles in data science, be prepared to discuss how you ensure ethical practices in your work. Share examples of how you've handled sensitive data responsibly and the measures you take to protect client information.
