ML Engineer: Transaction Monitoring for Banking (Glasgow)

ML Engineer: Transaction Monitoring for Banking (Glasgow)

Glasgow Full-Time 63000 - 77000 £ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Build and deploy machine learning solutions for transaction monitoring in Business Banking.
  • Company: Join TXP, a forward-thinking company in the banking sector.
  • Benefits: Competitive salary, flexible working days, and opportunities for professional growth.
  • Other info: Work onsite 2-3 days a week in a dynamic team environment.
  • Why this job: Make a real impact by turning data insights into actionable workflows.
  • Qualifications: Strong skills in Python, ML techniques, and experience with PySpark and SQL.

The predicted salary is between 63000 - 77000 £ per year.

TXP is seeking a Data Science / ML Engineer to help build and deploy machine learning solutions for transaction monitoring in Business Banking.

You will work with large-scale data, implement models, and collaborate with analytics colleagues to turn insights into actionable production workflows.

Candidates should be strong in Python, ML techniques, and data visualization, with experience in Py Spark and SQL.

  • Location is Glasgow (Edinburgh considered) and the role is onsite 2-3 days per week for a
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ML Engineer: Transaction Monitoring for Banking (Glasgow) employer: TXP

At TXP, we are committed to creating a dynamic and inclusive work environment where our employees can thrive. As a Senior Data Business Analyst in Birmingham, you will not only engage in meaningful projects within the pensions and insurance sectors but also benefit from our strong focus on professional development and social responsibility. Join us to be part of a team that values diversity, fosters growth, and makes a positive impact in the community.

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Contact Details:

TXP Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land ML Engineer: Transaction Monitoring for Banking (Glasgow)

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like TXP!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like ML Engineer: Transaction Monitoring for Banking (Glasgow) at TXP.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like TXP.

Apply Directly through Our Website

When you find a suitable opening like ML Engineer: Transaction Monitoring for Banking (Glasgow) at TXP, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace ML Engineer: Transaction Monitoring for Banking (Glasgow)

Python
Communication Skills
Problem-Solving Skills
Data Pipeline Development
Data Governance
Data Quality Assurance
SQL

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!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at TXP, 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 TXP. 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 TXP

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!

Showcase Your Projects

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 TXP!

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.