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.
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.
Your Team's Impact
FactSet's Funds Engineering team under DSE is focused on delivering Funds data (e.g., fund expense ratio, daily price & info data) coming from different sources by providing database solutions and automated processes which follow an Extract-Transform-Load (ETL) procedure. We develop & maintain an internal data collection application and manage funds data distribution services, routinely increasing efficiency year over year through automation.
Over time, the team has developed a robust and high-performing data pipeline capable of generating best in-class structured datasets that power FactSet’s products. Currently, the team is advancing this ecosystem by driving end-to-end automation across multiple stages of the pipeline. This includes the development of an event-driven, GenAI-centric, agent-based infrastructure aimed at enhancing efficiency, scalability, and innovation in data processing workflows.
What you’ll do
- Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
- Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
- Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
- Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
- Provide technical leadership to the development team, guide on architecture, design, and implementation decisions.
- Should lead and manage a technical team of 10 members.
- Provide technical support by incident resolution, and production troubleshooting
- Code quality through code reviews, advocating coding standards, and identifying risks before code reviews.
- Should collaborate with stakeholders and the healthy technical discussions engineering team.
- Supplements technical decision-making skills with broader business knowledge
What We’re Looking For
- 8-12 years of overall industry experience, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
- Bachelor’s or master’s degree in computer science or equivalent
- Mastery of Python to build ETL pipelines, object-oriented patterns, code optimization, automated testing, and packaging.
- Experience with Database Relational database (MYSQL, POSTGRES, MSSQL or similar RDBMS)
- Experience on Cloud Technology AWS/Azure/GCP etc.
- Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
- Effectively handle multiple projects, with different agendas and projects moving forward simultaneously
- Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
- Exceptional ability to explain complex technical subjects to diverse audiences.
- Excellent organization and time management skills (Time Management)
- Organized, self-directed, and resourceful with the ability to appropriately prioritize work in a fast-paced environment
- To effectively mentor others in the process for developing and ensuring high quality solutions
- Strong organizational and context switching ability
Desired Skills
- Experience in C++
- Experience with technologies like Pyspark , Databricks
- Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
- Secure cloud design, IAM, encryption, and compliance frameworks
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. Learn more at www.factset.com and follow us on X and LinkedIn.
At FactSet, we celebrate difference of thought, experience, and perspective. Qualified applicants will be considered for employment without regard to characteristics protected by law.
Skills Required
- 8–12 years of overall industry experience
- 3+ years in senior or lead roles delivering ML/AI solutions in a cloud environment
- Bachelor’s or master’s degree in computer science or equivalent
- Mastery of Python for ETL pipelines, object-oriented programming, optimization, automated testing, and packaging
- Experience with relational databases such as MySQL, PostgreSQL, Microsoft SQL Server, or similar RDBMS
- Experience with AWS, Azure, GCP, or comparable cloud technology
- Hands-on deployment and scaling of AI/ML services in the cloud
- Experience with containers, serverless technologies, and infrastructure as code
- Experience mentoring software engineers and leading system design and cross-team initiatives
- Ability to explain complex technical subjects to diverse audiences
- Experience with C++
- Experience with PySpark and Databricks
- Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance
- Experience with secure cloud design, IAM, encryption, and compliance frameworks
Factset Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Factset and has not been reviewed or approved by Factset.
-
Healthcare Strength — Healthcare coverage is positioned as comprehensive, spanning medical, dental/vision in some descriptions, and life and disability insurance. Company-wide wellness days and region-specific add-ons (e.g., Vitality PMI, Bupa dental, Health Shield cashback) further reinforce a strong health-and-wellbeing offering.
-
Retirement Support — Retirement savings support is consistently included as part of the core package through retirement savings plans. The presence of these programs is framed as a meaningful component of total rewards beyond base salary.
-
Equity Value & Accessibility — An employee stock purchase program is highlighted as a standard part of the total rewards package. This provides a pathway to share ownership alongside cash compensation.
Factset Insights
What We Do
FactSet creates flexible, open data and software solutions for tens of thousands of investment professionals around the world, providing instant access to financial data and analytics that investors use to make crucial decisions. For 40 years, through market changes and technological progress, our focus has always been to provide exceptional client service. From more than 60 offices in 23 countries, we’re all working together toward the goal of creating value for our clients, and we’re proud that 95% of asset managers who use FactSet continue to use FactSet, year after year. As big as we grow, as far as we reach, and as successful as we become, we stay connected to our clients and to each other.
