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For Companies At a Glance
- Tasks: Join our AI team to revolutionise financial data interaction using machine learning and natural language processing.
- Company: FactSet, a leading S&P 500 company with a culture of innovation and collaboration.
- Benefits: Enjoy flexible work arrangements, health benefits, and career development opportunities.
- Why this job: Make a real impact in finance by building intelligent systems that empower users with actionable insights.
- Qualifications: 3-5 years in software engineering with a focus on AI/ML, strong Python skills, and cloud architecture experience.
- Other info: Be part of a dynamic team that values creativity, mentorship, and continuous learning.
The predicted salary is between 30000 - 50000 £ per year.
FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, providing instant access to financial data and analytics that investors use to make crucial decisions.
Your Team's Impact
Join FactSet's Data Solutions AI team where our mission is to transform how financial professionals discover and interact with data through cutting-edge AI and machine learning technologies. We're building intelligent systems that enable clients to ask natural language questions about financial data and receive actionable insights powered by our semantic enrichment platform. At the core of our infrastructure is a knowledge graph that connects financial concepts to FactSet's comprehensive structured and unstructured data offerings, powering innovative solutions that push the boundaries of FactSet’s products.
What You'll Do
- Scale our unstructured financial document enrichment pipeline that powers FactSet’s knowledge graph to support the ingestion and enrichment of 500,000+ news, transcripts, and filings document chunks per day across dozens of financial domains.
- Enhance and scale our AWS-based infrastructure to ensure the efficient, reliable delivery of ML and AI projects, including the integration of agentic solutions.
- Work closely with other engineers and product developers to integrate and manage diverse domains of ML and NLP models. Offer expert advice on model selection and deployment strategies.
- In collaboration with agentic tooling (Claude Code, Cursor, CoPilot), manage the entire software development lifecycle, from the initial design and coding through to testing and the deployment of applications.
- Act as a mentor to team members, promoting a culture of innovation and continuous learning within the team.
What We're Looking For
You will be working on a team in a fast-paced environment where you will have the opportunity to influence the design and architecture of our products. An ideal candidate for the role would be an individual that has experience or a strong interest in working with data pipelines for unstructured data, a history of success deploying machine learning models to production, an interest in knowledge graph and semantic web technologies, and a passion for experimentation.
Required Skills
- 3-5 years of software engineering experience with significant focus on AI/ML solutions in production environments.
- Demonstrated expertise in cloud architecture, primarily AWS, with familiarity across a broad range of services.
- Strong understanding of Natural Language Processing, Machine Learning, and Deep Learning fundamentals with proven history of successful model development and deployment.
- Proficiency in Python, API development, and software engineering paradigms.
- Excellent communication abilities, capable of engaging both technical and business audiences and leading cross-functional projects.
- Knowledge of major database architectures including SQL, NoSQL, and Vector databases.
Desired Skills
- Experience with Knowledge Graphs and architecting LLM-powered solutions.
- Familiarity with financial data, applications, and specific industry challenges.
- Expertise in NLP libraries such as nltk and SpaCy and proficiency in unstructured text analysis.
- Demonstrable leadership capabilities and experience in mentoring or leading a team.
What's In It For You
At FactSet, our people are our greatest asset, and our culture is our biggest competitive advantage. Being a FactSetter means:
- The opportunity to join an S&P 500 company with over 45 years of sustainable growth powered by the entrepreneurial spirit of a start-up.
- Support for your total well-being. This includes health, life, and disability insurance, as well as retirement savings plans and a discounted employee stock purchase program, plus paid time off for holidays, family leave, and company-wide wellness days.
- Flexible work accommodations. We value work/life harmony and offer our employees a range of accommodations to help them achieve success both at work and in their personal lives.
- A global community dedicated to volunteerism and sustainability, where collaboration is always encouraged, and individuality drives solutions.
- Career progression planning with dedicated time each month for learning and development.
Salary is just one component of our compensation package and is based on several factors including but not limited to education, work experience, and certifications.
Use of AI tools during the interview process
The landscape of AI tools is rapidly evolving and FactSet engineers use AI tools extensively as a part of their daily work. During the interview process, you are encouraged to use AI tools like ChatGPT, Claude, Copilot when:
- Refining your resume and cover letter for submission
- Preparing for your interview, and researching FactSet and its products
You may not use AI tools:
- During an interview, or
- When explicitly requested not to use AI tools
Company Overview
FactSet (NYSE:FDS | NASDAQ:FDS) helps the financial community to see more, think bigger, and work better. Our digital platform and enterprise solutions deliver financial data, analytics, and open technology to more than 8,200 global clients, including over 200,000 individual users. Clients across the buy-side and sell-side, as well as wealth managers, private equity firms, and corporations, achieve more every day with our comprehensive and connected content, flexible next-generation workflow solutions, and client-centric specialized support. As a member of the S&P 500, we are committed to sustainable growth and have been recognized among the Best Places to Work in 2023 by Glassdoor as a Glassdoor Employees’ Choice Award winner. At FactSet, we celebrate difference of thought, experience, and perspective. Qualified applicants will be considered for employment without regard to characteristics protected by law. At FactSet, our values are the foundation of everything we do. They express how we act and operate, serve as a compass in our decision-making, and play a big role in how we treat each other, our clients, and our communities. We believe that the best ideas can come from anyone, anywhere, at any time, and that curiosity is the key to anticipating our clients’ needs and exceeding their expectations.
Machine Learning Engineer - Python, SQL, NoSQL, and Vector databases in London employer: FactSet Research Systems Inc.
Contact Detail:
FactSet Research Systems Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - Python, SQL, NoSQL, and Vector databases in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at FactSet. LinkedIn is your best mate here—send personalised messages and ask for informational chats. You never know who might put in a good word for you!
✨Tip Number 2
Prepare for those technical interviews! Brush up on your Python, SQL, and machine learning concepts. Practise coding challenges and be ready to discuss your past projects. We want to see your problem-solving skills in action!
✨Tip Number 3
Show off your passion for AI and ML! During interviews, share your thoughts on the latest trends and how they can impact the financial sector. This will demonstrate your enthusiasm and keep the conversation engaging.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the FactSet family!
We think you need these skills to ace Machine Learning Engineer - Python, SQL, NoSQL, and Vector databases in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Machine Learning Engineer role. Highlight your experience with Python, SQL, and NoSQL, and don’t forget to mention any relevant projects that showcase your skills in AI and ML.
Showcase Your Passion: Let us see your enthusiasm for machine learning and data pipelines! Share any personal projects or experiments you've worked on, especially those involving unstructured data or knowledge graphs. This will help you stand out!
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless necessary. We want to understand your experience and skills without having to decipher complex terms.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way to ensure it gets into the right hands. Plus, you’ll find all the details about the role and our company culture there.
How to prepare for a job interview at FactSet Research Systems Inc.
✨Know Your Tech Stack
Make sure you’re well-versed in Python, SQL, NoSQL, and Vector databases. Brush up on your knowledge of AWS services too, as they play a crucial role in the infrastructure at FactSet. Being able to discuss your experience with these technologies confidently will show that you're ready to hit the ground running.
✨Showcase Your ML Experience
Prepare to discuss specific machine learning projects you've worked on, especially those involving unstructured data. Be ready to explain your model selection process, deployment strategies, and any challenges you faced. This will demonstrate your hands-on experience and problem-solving skills.
✨Communicate Clearly
Since you'll be engaging with both technical and business audiences, practice explaining complex concepts in simple terms. Use examples from your past work to illustrate your points. Good communication can set you apart, especially in a collaborative environment like FactSet.
✨Emphasise Continuous Learning
FactSet values innovation and growth, so share your passion for learning new technologies and methodologies. Discuss any recent courses, certifications, or personal projects that showcase your commitment to staying current in the field of AI and machine learning.
