American Express Data Science Job Openings 2025 – Hybrid & WFH Options Available
Data Science Job Openings at American Express – Analyst Position in Gurgaon
American Express, a leading global financial services company, is currently hiring for the position of Analyst – Data Science at its Gurgaon, Haryana, India office. This exciting opportunity is ideal for individuals with a strong background in data science and machine learning, looking to work in a dynamic, forward-thinking environment.
Job Overview
As an Analyst in Data Science, you will be part of the GSG Advanced Analytics team at American Express. This role requires a passion for data science, machine learning, and NLP (Natural Language Processing). You will collaborate with cross-functional teams to solve complex business problems and drive actionable insights through advanced analytics.
Key Responsibilities
- Apply machine learning techniques such as Regression, Classification, and Supervised/Unsupervised Recommenders to real-world business problems.
- Contribute to deep learning projects, including working with Transformers, LSTMs, and CNNs.
- Develop and implement data wrangling and visualization techniques to ensure the accuracy and clarity of data.
- Work with large datasets to extract actionable insights, creating scalable solutions for the business.
- Conduct rigorous algorithm testing to ensure efficient business operations and measurable improvements.
- Engage with and apply cutting-edge algorithms in NLP and deep learning to enhance business outcomes.
- Collaborate with cross-functional teams to translate business problems into data science and machine learning roadmaps.
- Stay updated on the latest advancements in machine learning, NLP, and AI technologies to keep the team at the forefront of innovation.
Qualifications
Minimum Requirements
- Bachelor’s or Master’s degree in a quantitative field such as Finance, Engineering, Mathematics, Statistics, Computer Science, or Economics.
- Proven experience applying machine learning techniques such as regression, classification, and recommendation systems.
- Hands-on knowledge of SQL and proficiency in data wrangling and visualization tools.
- A research-driven mindset with a focus on experimenting with new algorithms.
- Experience with Python and related libraries (e.g., pandas, numpy, tensorflow, pytorch).
- Excellent understanding of deep learning and NLP techniques, including working with Large Language Models (LLMs) is a plus.
Preferred Qualifications
- A Master’s degree from a top institute with research papers on deep learning or NLP.
- Familiarity with frameworks like TensorFlow, PyTorch, and experience fine-tuning Transformers/LSTMs/CNNs.
- Strong understanding of statistical and mathematical principles, including exposure to linear algebra and Bayesian statistics.
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Skills Required
- Deep expertise in the Python environment, including libraries like pandas, numpy, nltk, spacy, pyspark, transformers, and more.
- Knowledge of performance testing and validation frameworks for machine learning models.
- Ability to work with terabyte-sized data and design scalable solutions.
- Hands-on experience with large-scale machine learning models and implementing real-world business applications.
Work Environment & Benefits
American Express offers a hybrid work model (onsite and virtual options), allowing flexibility to employees based on the business requirements and role specifics. The company also provides an excellent benefits package, including:
- Competitive salary and bonus incentives.
- Comprehensive medical, dental, and life insurance benefits.
- Paid parental leave and access to global wellness centers.
- Career development opportunities with training programs.
- Access to financial well-being support and retirement planning.
How to Apply
If you are a passionate data scientist with a solid foundation in machine learning and NLP, we encourage you to apply for this opportunity to join the American Express team as an Analyst in Data Science. Enhance your career while working on impactful projects that shape the future of data-driven decision-making in a leading global company.
