Data Scientist: ML & NLU Solutions Architect in London

Data Scientist: ML & NLU Solutions Architect in London

London Full-Time 63000 - 77000 £ / year (est.) No working from home possible
NICE

At a Glance

  • Tasks: Develop AI solutions and improve model performance through ML experimentation.
  • Company: Join NiCE, a forward-thinking company focused on innovative data solutions.
  • Benefits: Enjoy competitive pay, flexible work options, and opportunities for growth.
  • Other info: Collaborative environment with a focus on professional development.
  • Why this job: Make a real impact by solving business problems with cutting-edge technology.
  • Qualifications: Experience in machine learning and strong analytical skills required.

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

Ni CE is seeking a Data Scientist to join our team and help develop machine learning and AI solutions that support customer operations.

The role covers NLU, ML experimentation, and high-quality training data preparation to improve model performance.

You will work across the full ML lifecycle, building and evaluating models, running experiments, and delivering data-driven solutions to business problems while collaborating with stakeholders for impact.

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Data Scientist: ML & NLU Solutions Architect in London employer: NICE

NiCE is an exceptional employer that fosters a collaborative and innovative work culture, offering a hybrid working model that balances office and remote work. Employees benefit from continuous growth opportunities in cloud technologies, supported by a team of skilled professionals dedicated to excellence in AWS environments. Located in the United Kingdom, NiCE provides a dynamic environment where your contributions are valued and your career can thrive.

NICE

Contact Details:

NICE Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Scientist: ML & NLU Solutions Architect in London

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 NICE!

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 Data Scientist: ML & NLU Solutions Architect at NICE.

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 NICE.

Apply Directly through Our Website

When you find a suitable opening like Data Scientist: ML & NLU Solutions Architect at NICE, 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 Data Scientist: ML & NLU Solutions Architect in London

Machine Learning
Natural Language Understanding (NLU)
Data Preparation
Model Evaluation
Experimentation
Data-Driven Solutions
Stakeholder Collaboration

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 NICE, 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 NICE. 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 NICE

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 NICE!

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.