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- Tasks: Join a dynamic team to develop machine learning models and optimise data solutions.
- Company: Amach, a fast-growing tech company revolutionising air travel.
- Benefits: Flexible working, competitive salaries, and great career advancement opportunities.
- Why this job: Make a real impact in the future of air travel with cutting-edge technology.
- Qualifications: Strong Python skills and knowledge of machine learning techniques required.
- Other info: Inclusive culture that values diversity and innovation.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Build the Future of Air Travel with Amach. Join one of the world’s fastest-growing technical teams, where innovation meets impact. We take the time to understand your skills, ambitions, and what truly drives you—because your journey matters. Amach is an industry‑leading technology‑driven company headquartered in Dublin with remote teams in the UK and Europe. Our blended teams of local and nearshore talent deliver high quality and collaborative solutions. Founded in 2013, we specialise in cloud migration, agile software development, DevOps, automation, data and machine learning, and digital transformation.
As a key member of a product squad reporting to the Lead Product Data Scientist, a Data Scientist will develop data pipelines, machine learning models and complex optimisation models in the ODS software product suite. The Data Scientist is in charge of modelling and robust implementation of features contributing to an operations decision‑support product. In developing a product’s core algorithm, the full‑stack Data Scientist role ensures that features integrate seamlessly into the product’s technical stack (data ingestion, user interface, orchestration) as well as the business process and use case.
Required Skills
- Strong knowledge of machine learning and optimisation techniques, including supervised (regression, tree methods, etc.), unsupervised (clustering), and operations research (linear, mixed integer programming, heuristics)
- Fluent in Python (required) and other programming languages (preferred) with strong skills in applying DS, ML, and OR packages (scikit‑learn, pandas, numpy, gurobi, etc.) to solve real‑life problems and visualise the outcomes (e.g., seaborn)
- Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g., MLflow)
- Experience with cloud‑based ML tools (e.g., SageMaker), data and model versioning (e.g., DVC), CI/CD (e.g., GitHub Actions), workflow orchestration (e.g., Airflow/Dagster) and containerised solutions (e.g., Docker, ECS) nice to have
- Experience in code testing (unit, integration, end‑to‑end tests)
- Strong data engineering skills in SQL and Python
- Proficient in use of Microsoft Office, including advanced Excel and Powerpoint skills
- Advanced analytical skills, including the ability to apply a range of data science and analytic techniques to quickly generate accurate business insights
- Understanding of the trade‑offs of different data science, machine learning, and optimisation approaches, and ability to intelligently select which are the best candidates to solve a particular business problem
- Able to structure business and technical problems, identify trade‑offs, and propose solutions
- Communication of advanced technical concepts to audiences with varying levels of technical skills
- Managing priorities and timelines to deliver features in a timely manner that meet business requirements
- Collaborative team‑working, giving and receiving feedback, and always seeking to improve team processes
Accountabilities
- Understand a business problem and its component processes end to end, and identify opportunities to make decisions more optimally leveraging decision‑support tooling
- Efficiently conduct analyses and visualisations to identify valuable opportunities for decision‑support and to determine trade‑offs between different potential feature implementations
- Prototype advanced machine learning and optimisation models to prove the value of a use‑case and approach (in Python)
- Deliver features to industrialise machine learning and optimisation models in Python using best‑practice software principles (e.g., strict typing, classes, testing)
- Build automated, robust data cleaning pipelines that follow software best‑practices (in Python)
- Implement integrations between the core algorithm (machine‑learning or optimisation) and a workflow orchestration paradigm such as Dagster
- Implement software in a cloud‑based deployment pipeline with Continuous Integration / Continuous Deployment (CI/CD) principles
- Build logging, error handling, and automated tests (e.g., unit tests, regression tests) to ensure the robustness of operationally critical decision‑support products
- Deliver features to harden an algorithm against edge cases in the operation and in data
- Conduct analysis to quantify the adoption and value‑capture from a decision‑support product
- Engage with business stakeholders to collect requirements and get feedback
- Contribute to conversations on feature prioritisation and roadmap, with an understanding of the trade‑off between speed vs. long‑term value
- Understand and integrate the product into existing business processes, and contribute to the development and adoption of new business processes leveraging a decision‑support product
- Communicate feature and modelling approach, trade‑offs, and results with the internal team and business stakeholders
Ways of Working
- Using Git‑versioning best practices for version control
- Contributing and reviewing pull‑requests and product / technical documentation
- Giving input on prioritisation, team process improvements, optimising technology choices
- Working independently and giving predictability on delivery timelines
- Systems thinking
- Detail‑oriented while understanding the big picture
- Curious, self‑motivated, proactive, and action‑oriented
- Creative and innovative
- Resilient and flexible in light of changing priorities and approaches
- Data‑driven
- Pragmatic
- A true believer in the power of using data to drive better decision making
- A technologist, interested in keeping up with the latest and greatest in software development, optimisation, and machine learning
- Commitment to delivering business value
What’s in it for you
- An opportunity to join a fast‑growing company
- Options for career advancement
- Learning and development opportunities
- Flexible working environment
- Competitive salaries based on experience
Equal Opportunity Employer: Amach is an equal opportunity employer and makes employment decisions on the basis of merit. We celebrate diversity and are committed to creating an inclusive environment for all employees. This job description is intended to convey essential responsibilities and qualifications for this role, but it is not an exhaustive list of tasks that an employee may be required to perform.
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Data Scientists New London, England, United Kingdom employer: Amach
Contact Detail:
Amach Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientists New London, England, United Kingdom
✨Tip Number 1
Network like a pro! Reach out to current employees at Amach on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing a role there. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for the interview by brushing up on your technical skills. Make sure you can confidently discuss machine learning models, Python coding, and data visualisation techniques. Practice explaining complex concepts in simple terms—this will impress your interviewers!
✨Tip Number 3
Showcase your projects! If you've worked on relevant data science projects, be ready to discuss them in detail. Highlight your problem-solving approach and the impact of your work. This is your chance to shine and demonstrate your skills!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in joining the team at Amach. Good luck!
We think you need these skills to ace Data Scientists New London, England, United Kingdom
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Data Scientist role. Highlight your experience with machine learning, Python, and any relevant projects that showcase your skills. We want to see how you fit into our team!
Showcase Your Skills: Don’t just list your skills—demonstrate them! Include specific examples of how you've used data science techniques in real-life scenarios. This helps us understand your problem-solving approach and technical expertise.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless necessary. We appreciate a well-structured application that gets straight to the point—this shows us you can communicate effectively!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application reaches us quickly and efficiently. Plus, you’ll find all the details about the role and our company there!
How to prepare for a job interview at Amach
✨Know Your Tech Inside Out
Make sure you’re well-versed in the machine learning and optimisation techniques mentioned in the job description. Brush up on your Python skills and be ready to discuss how you've applied libraries like scikit-learn and pandas in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific business problems you've tackled using data science. Think about how you structured these problems, the trade-offs you considered, and the solutions you proposed. Real examples will make your answers stand out!
✨Communicate Clearly
You’ll need to explain complex technical concepts to non-technical stakeholders. Practice simplifying your explanations without losing the essence of your work. This will show that you can bridge the gap between tech and business effectively.
✨Demonstrate Team Spirit
Amach values collaboration, so be ready to talk about your experiences working in teams. Share how you’ve given and received feedback, and how you’ve contributed to improving team processes. Highlighting your collaborative nature will resonate well with them.
