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- Tasks: Develop machine learning models for classification and prediction while collaborating with stakeholders.
- Company: Leading data integration firm in the UK with a focus on equity and impact.
- Benefits: Competitive salary, equity, and 30+ days of paid time off annually.
- Why this job: Make a real impact by translating business goals into innovative ML solutions.
- Qualifications: 3+ years of experience in machine learning and fluency in Python.
- Other info: Join a collaborative environment with opportunities for professional growth.
The predicted salary is between 28800 - 48000 £ per year.
A leading data integration firm in the UK is seeking a Machine Learning Engineer to develop models for classification, prediction, and more. You will translate business goals into modeling objectives and work with stakeholders to evaluate data sources.
The ideal candidate has over 3 years of experience, is fluent in Python, and is comfortable in a collaborative environment.
The role offers a competitive salary, equity, and at least 30 days of paid time off per year.
Remote ML Engineer — Equity & Impact employer: HASH
Contact Detail:
HASH Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Remote ML Engineer — Equity & Impact
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. This is your chance to demonstrate your Python prowess and how you've tackled real-world problems.
✨Tip Number 3
Prepare for those interviews! Brush up on common ML concepts and be ready to discuss how you’ve translated business goals into modelling objectives. Practice makes perfect, so do mock interviews with friends!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to get noticed by our hiring team.
We think you need these skills to ace Remote ML Engineer — Equity & Impact
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning and Python. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about machine learning and how you can contribute to our mission. Keep it engaging and personal – we love to see your personality!
Showcase Collaboration Skills: Since this role involves working with stakeholders, make sure to mention any collaborative projects you've been part of. We value teamwork, so share examples that demonstrate your ability to work well with others.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at HASH
✨Know Your Models
Make sure you brush up on the different machine learning models and their applications. Be ready to discuss how you've used classification and prediction models in your previous roles, and think of specific examples that showcase your expertise.
✨Understand the Business Goals
Since you'll be translating business goals into modelling objectives, it’s crucial to understand the company’s mission and how they leverage data. Research the firm’s projects and think about how your skills can align with their objectives.
✨Showcase Your Python Skills
Fluency in Python is a must for this role. Prepare to demonstrate your coding skills during the interview. You might be asked to solve a problem or explain your thought process while coding, so practice common algorithms and data manipulation tasks.
✨Collaborate and Communicate
As the role requires working with stakeholders, highlight your experience in collaborative environments. Be prepared to discuss how you’ve effectively communicated complex technical concepts to non-technical team members in the past.
