FactSet creates adaptable, 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
We’re seeking a passionate and experienced Senior Python & Machine Learning Engineer to join our Data domain team. You’ll work on unique, one-of-a-kind problem statements using advanced GenAI, large language models (LLMs), and modern data engineering frameworks. You will help conceptualize and deliver impactful solutions that push the boundaries of data science and machine learning in finance.

What You'll Do :
- Design, develop,



and deploy sophisticated machine learning and GenAI models to solve complex data problems at scale.
- Implement, optimize, and scale ML solutions using Databricks, Spark, and cloud-native data ecosystems (AWS/Azure/GCP).
- Collaborate with other engineers, product managers, and UX teams to build robust, high-performance Python-based analytics pipelines.
- Develop and finetune LLMs and generative AI applications for structured and unstructured financial data.
- Architect data processing workflows leveraging Delta Lake, Feature Stores, and MLOps best practices.
- Translate cutting-edge research (papers, new ML techniques) into production solutions.
- Mentor junior data scientists and engineers on ML, best practices and GenAI.
- Work on one-of-a-kind data challenges, including entity disambiguation, real-time risk analytics, NLP, graph data modeling, and anomaly detection.
- Keep up-to-date with the latest in ML tooling

📌 Software Engineer III (Hyderabad)
🏢 FACTSET
📍 Hyderabad