Junior Data Scientist
AI in this role
Junior Data Scientist responsible for developing machine learning models, data pipelines, and quantitative analytics solutions for financial products.
Design and develop new methodologies, quantitative models, analysis and commentary (as relevant) that enhance existing or new processes. Maintain existing product performance and independent analysis standards. Internally, work with various departments to grow new ideas and expand the scope of existing products.
Job Description
- Data handling, data processing and programming
- Develop automated solutions for sourcing/loading Entity data into Database
- Analyse Legal entity content to establish patterns/trends
- Develop, improve and run quantitative models
- Generate solutions through machine learning for core processes
- Work with business, content and product groups for large-scale analytics problems
- Build POCs, visualizations and pipeline tools for product design and development
- Work with development groups for deployment of analytics solutions
- Interact with other internal teams as needed, with supervision.
Knowledge & Skill:
- Extensive experience on using Python, Power Apps, SQL…
- Proficient in using tools and libraries such as scikit-learn, NumPy, pandas and jupyter
- Solid relational database skills
- Solid understanding of statistics and statistical language such as R
- Ability to handle large quantities of data
- Narrate stories (to technical and mostly non-technical audience) about our content and processes by data analysis and visualization
- Strong written, communication and presentation skills. Able to respond and present work to peers, senior management and other stakeholders
- Certified (or attempting) as a Lean / Six Sigma green/black belt with practical experience in Lean Project Management along with practical experience in coaching, facilitation, Lean deployments, and Lean projects
- Project management or/and Process optimization experiences (can have been informal as part of wider roles and responsibilities, but must be demonstrable if so)
Qualification:
- Higher education in Statistics, Mathematics or Engineering in Computer Science with Data science certification
Desired Skills:
- 3+ years of Data Science project experience in Finance domain is preferred
Career Stage:
Senior AssociateLondon Stock Exchange Group (LSEG) Information:
Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.
LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth.
Our purpose is the foundation on which our culture is built. Our values of Integrity, Partnership, Excellence and Change underpin our purpose and set the standard for everything we do, every day. They go to the heart of who we are and guide our decision making and everyday actions.
Working with us means that you will be part of a dynamic organisation of 25,000 people across 65 countries. However, we will value your individuality and enable you to bring your true self to work so you can help enrich our diverse workforce.
We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone’s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
You will be part of a collaborative and creative culture where we encourage new ideas. We are committed to sustainability across our global business and we are proud to partner with our customers to help them meet their sustainability objectives. Our charity, the LSEG Foundation provides charitable grants to community groups that help people access economic opportunities and build a secure future with financial independence. Colleagues can get involved through fundraising and volunteering.
LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
Please take a moment to read this privacy notice carefully, as it describes what personal information London Stock Exchange Group (LSEG) (we) may hold about you, what it’s used for, and how it’s obtained, your rights and how to contact us as a data subject.
If you are submitting as a Recruitment Agency Partner, it is essential and your responsibility to ensure that candidates applying to LSEG are aware of this privacy notice.
How we rate this
Junior Data Scientist at LSEG rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
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Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where data science was part of your work. What did you do?
- Tell me about a project where data processing was part of your work. What did you do?
- Tell me about a project where quantitative modeling was part of your work. What did you do?
- Tell me about a project where statistics was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Machine Learning, Data Science, Data Processing, Quantitative Modeling, and Statistics. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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