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- Tasks: Use data science to solve real business problems and drive impactful decisions.
- Company: Global company transforming industries with innovative data solutions.
- Benefits: Flexible work options, continuous learning, and a supportive environment.
- Why this job: Make a difference by leveraging data to influence key business strategies.
- Qualifications: 5+ years in data science, strong Python/R skills, and a passion for problem-solving.
- Other info: Join a diverse team committed to inclusion and personal growth.
The predicted salary is between 36000 - 60000 ÂŁ per year.
What you'll be doing:
- Partner with business stakeholders to identify and prioritise opportunities where data science can deliver measurable value.
- Collect, clean, and transform structured and unstructured data from multiple internal and external sources.
- Develop, test, and deploy predictive models and machine learning algorithms to address business challenges.
- Conduct exploratory data analysis (EDA) to uncover trends, patterns, anomalies, and key drivers.
- Communicate insights and recommendations through clear storytelling, visualisations, and dashboards.
- Collaborate with engineering teams to productionise models and ensure reliability, scalability, and ongoing performance.
- Evaluate model accuracy and effectiveness, implementing continuous optimisation and tuning.
- Stay up to date with emerging data science tools, methodologies, and industry best practices.
- Perform sensitivity analysis to assess model robustness and variable impact.
What experience you'll bring:
- At least 5 years' experience in clientâfacing data science roles with demonstrable impact on business outcomes.
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related discipline.
- Strong proficiency in Python or R, including libraries such as pandas, scikitâlearn, NumPy, TensorFlow, or PyTorch.
- Solid understanding of statistical analysis, hypothesis testing, and experimental design.
- Handsâon experience applying a range of supervised and unsupervised machine learning techniques (e.g., Random Forest, regression models, clustering methods).
- Proficiency with SQL and data warehousing technologies.
- Ability to translate complex analytical findings into clear, practical business recommendations.
- Strong problemâsolving skills and natural curiosity for exploring and understanding data.
Preferred Skills and Qualifications:
- Experience working with cloud platforms such as Azure, AWS, or Google Cloud.
- Background in deploying machine learning models into production environments (MLOps experience is advantageous).
- Handsâon experience with bigâdata or distributed computing tools such as Spark or Databricks.
- Familiarity with visualisation tools such as Power BI, Tableau, or Plotly.
- Industry experience in sectors such as retail, finance, healthcare, or similar (customisable).
Key Competencies:
- Strong analytical and conceptual thinking.
- Excellent communication and dataâstorytelling capabilities.
- Effective collaboration and stakeholderâengagement skills.
- High attention to detail and commitment to data accuracy.
Who we are:
We're a business with a global reach that empowers local teams, and we undertake hugely exciting work that is genuinely changing the world. Our advanced portfolio of consulting, applications, business process, cloud, and infrastructure services will allow you to achieve great things by working with brilliant colleagues, and clients, on exciting projects. Our inclusive work environment prioritises mutual respect, accountability, and continuous learning for all our people. This approach fosters collaboration, wellâbeing, growth, and agility, leading to a more diverse, innovative, and competitive organisation.
What we'll offer you:
We offer a range of tailored benefits that support your physical, emotional, and financial wellbeing. Our Learning and Development team ensure that there are continuous growth and development opportunities for our people. We also offer the opportunity to have flexible work options. We are an equal opportunities employer. We believe in the fair treatment of all our employees and commit to promoting equity and diversity in our employment practices. We are also a proud Disability Confident Committed Employer - we are committed to creating a diverse and inclusive workforce. We actively collaborate with individuals who have disabilities and long-term health conditions which have an effect on their ability to do normal daily activities, ensuring that barriers are eliminated when it comes to employment opportunities. In line with our commitment, we guarantee an interview to applicants who declare to us, during the application process, that they have a disability and meet the minimum requirements for the role. If you require any reasonable adjustments during the recruitment process, please let us know. Join us in building a truly diverse and empowered team.
Data Scientist in London employer: NTT America, Inc.
Contact Detail:
NTT America, Inc. Recruiting Team
StudySmarter Expert Advice đ¤Ť
We think this is how you could land Data Scientist in London
â¨Tip Number 1
Network like a pro! Reach out to your connections in the data science field and let them know you're on the lookout for opportunities. You never know who might have a lead or can refer you to a hiring manager.
â¨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving predictive models and machine learning. This will give potential employers a taste of what you can do and how you can add value.
â¨Tip Number 3
Prepare for interviews by brushing up on your storytelling skills. Be ready to explain your data analysis process and how your insights led to business improvements. Practice makes perfect!
â¨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are excited about joining our team. Plus, it gives you a better chance to stand out!
We think you need these skills to ace Data Scientist in London
Some tips for your application đŤĄ
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience in data science, especially how it aligns with the role. We want to see how your skills can deliver measurable value, so donât hold back on showcasing your achievements!
Showcase Your Technical Skills: Since this role requires strong proficiency in Python or R, be sure to mention specific projects where you've used these tools. We love seeing hands-on experience with machine learning techniques, so include any relevant examples that demonstrate your expertise.
Communicate Clearly: Remember, weâre looking for someone who can translate complex data findings into clear business recommendations. Use your application to tell a story about your data journey, and donât forget to include any visualisation tools youâve worked with!
Apply Through Our Website: We encourage you to apply directly through our website. Itâs the best way for us to receive your application and ensures youâre considered for the role. Plus, it shows youâre keen to join our team at StudySmarter!
How to prepare for a job interview at NTT America, Inc.
â¨Know Your Data Science Tools
Make sure you're well-versed in the tools and libraries mentioned in the job description, like Python, R, and SQL. Brush up on your knowledge of machine learning techniques and be ready to discuss how you've applied them in real-world scenarios.
â¨Prepare for Case Studies
Expect to tackle case studies or practical problems during the interview. Practice explaining your thought process clearly, from data collection to model deployment. This will showcase your analytical skills and problem-solving abilities.
â¨Communicate Like a Pro
Since storytelling with data is key, practice how you present insights. Use visualisations to back up your points and ensure you can explain complex concepts in simple terms. This will demonstrate your ability to translate data into actionable business recommendations.
â¨Show Your Collaborative Spirit
Highlight your experience working with cross-functional teams. Be prepared to discuss how you've collaborated with stakeholders and engineering teams to bring models to production. This shows that you value teamwork and understand the importance of stakeholder engagement.