Senior Software Engineer - Python with AI ML

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

Join our dynamic Machine Learning and AI team, where we build innovative models and solutions that drive business transformation and unlock new opportunities. You’ll be at the forefront of AI-powered initiatives, collaborating closely with product teams to shape the future of data-driven insights. This position offers high visibility and the chance to directly influence key decisions across the organization. You will report to the Manager of AI.

Working Mode: Hybrid (3 days mandatory Office)

What You'll Do

• Technical Leadership & Strategy

• Lead the design, development, and deployment of machine learning models and systems at scale.

• Define and drive the technical roadmap for ML initiatives in alignment with business goals.

• Evaluate and select appropriate ML techniques, architectures, and tools for various problems (e.g., NLP, CV, tabular data).

• Ensure robust experimentation, validation, and performance benchmarking practices.

• Team Guidance & Mentorship

• Mentor and support junior and mid-level ML engineers, guiding them on model development, research approaches, and code quality.

• Conduct technical reviews of models, pipelines, and code to ensure high standards.

• Promote a culture of continuous learning, innovation, and scientific rigor within the team.

• System & Pipeline Development

• Architect and implement scalable ML pipelines for training, validation, inference, and monitoring.

• Collaborate with data engineers to ensure high-quality data ingestion, feature engineering, and labeling workflows.

• Contribute to MLOps practices by building reproducible, testable, and maintainable model delivery frameworks.

• Assist in designing, developing, and implementing machine learning models for real-world applications. 

• Work on data collection, preprocessing, feature engineering, and model evaluation tasks. 

• Collaborate with cross-functional teams including Data Science, Software Engineering, and Product. 

• Perform exploratory data analysis (EDA) and prepare datasets for training/testing. 

• Contribute to the deployment and monitoring of models in production environments. 

• Write clean, efficient, and well-documented code in Python or similar languages. 

• Stay updated with the latest developments in AI/ML research and tools. 

• Assist in model optimization, hyperparameter tuning, and performance scaling. 

• Stay current with the latest industry trends and technologies, contributing innovative ideas to ongoing projects. 

• Test and validate models to ensure their reliability and effectiveness in production environments. 

• Work with large datasets to extract meaningful insights using various statistical and ML techniques.

What We're Looking For

Required Skills 

• Bachelor’s or master’s degree in computer science, Engineering, or a related field is required. 

• 5+ years of experience in software development, with a focus on systems handling large-scale data operations. 

• Strong foundation in Machine Learning concepts (supervised/unsupervised learning, regression, classification, clustering, etc.).

• Good programming skills in Python (or similar languages like R, Java, C++).

• Hands-on experience with ML libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Keras).

• Understanding of data structures, algorithms, and basic mathematics/statistics and database management systems.

• Excellent verbal and written communication skills, capable of articulating complex concepts to technical and non-technical audiences.

• Familiarity with data handling tools (e.g., Pandas, NumPy, SQL).

• Good analytical, problem-solving, and communication skills.

• Ability to learn new technologies quickly and work independently or as part of a team.

• Ability to work collaboratively in a team environment, contributing to group success while expanding personal skills.

Desired Skills