American Express is hiring for a fresher entry level Data Scientist role in India

Position:

• Analyst - Data Science

• Focused on data-driven problem-solving using machine learning and natural language processing

• Contributing to advanced analytics and automation in servicing operations

Company:

• American Express

• A global financial services leader focused on customer service, innovation, and employee support

• Recognized for driving customer satisfaction and employee growth opportunities

Location:

• Gurugram, Haryana, India

Job type:

• Full-time position

Job mode:

• Hybrid (combination of in-office and remote work)

Job requisition id:

• 24019445

Years of experience:

• 0 to 3 years of relevant experience

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Company Description

• American Express is a globally renowned leader in financial services, offering an inclusive environment where employees are empowered to thrive personally and professionally.

• With a culture rooted in integrity, innovation, and customer-centricity, Amex fosters strong internal collaboration and a deep sense of purpose.

• The company emphasizes diversity, respect, and recognition, creating a space where all employees are heard, seen, and valued.

• Known for supporting career progression, American Express provides tailored growth plans, mentorship, and learning platforms.

• Employees benefit from a workplace that prioritizes well-being and flexibility, backed by comprehensive policies that balance work and life.

• With a presence in numerous countries, Amex is committed to local communities and global progress, enabling both individuals and businesses to grow confidently.

• As an equal opportunity employer, it welcomes individuals from all walks of life and supports them throughout their journey at the company.

• Whether through digital innovation, outstanding customer service, or empowering employee initiatives, American Express maintains its position as a pioneer in the global financial ecosystem.

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Profile Overview

• This role sits within the Global Services Group (GSG) Advanced Analytics team, which focuses on developing and deploying data science solutions to improve internal servicing processes.

• The selected candidate will work on diverse machine learning and AI projects aligned with multiple business verticals within the GSG.

• Key responsibilities include translating complex business problems into actionable data science initiatives and delivering high-impact results through statistical models and NLP applications.

• Candidates will be expected to collaborate with cross-functional teams and stakeholders to define problem statements and derive strategic insights.

• A strong focus is placed on designing scalable models capable of handling large volumes of structured and unstructured data efficiently.

• Emphasis will be on research, experimentation, and rapid prototyping of advanced algorithms to drive operational excellence.

• Candidates will also be engaged in deploying these models into production and evaluating their performance using well-defined metrics.

• The environment encourages continuous learning, exploration of cutting-edge technologies, and hands-on coding to maintain analytical precision.

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Qualifications

• Must be proficient in Python, with demonstrated expertise in relevant libraries including pandas, numpy, statsmodels, nltk, spacy, gensim, transformers, and pyspark.

• Hands-on experience in applying machine learning models such as supervised and unsupervised algorithms, regression, classification, and recommender systems.

• Strong knowledge of SQL for data extraction, transformation, and processing from large databases.

• Prior experience with deep learning architectures such as CNNs, RNNs, LSTMs, and Transformer models is considered advantageous.

• Exposure to fine-tuning Large Language Models (LLMs) will be a valuable asset in handling NLP-centric tasks.

• Strong foundational understanding of mathematics and statistics, including linear algebra, probability theory, group theory, and Bayesian statistics.

• Working knowledge of tools like TensorFlow or PyTorch for model development and deployment.

• Must have excellent data wrangling and data visualization capabilities to clearly communicate results to non-technical stakeholders.

• Demonstrated ability to work within multidisciplinary teams and maintain productivity across diverse projects.

• A learning mindset and a research-oriented approach toward integrating new techniques and frameworks into existing workflows.

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Additional Info

• The role provides ample opportunities for career development through internal mentorship programs, workshops, and industry certifications.

• American Express supports employee well-being through medical, dental, vision, and life insurance coverage, alongside disability benefits.

• Employees can take advantage of Amex Flex, which offers flexible and hybrid work schedules designed for work-life balance.

• Generous parental leave policies, wellness centers, and on-site healthcare services are provided at various locations.

• The organization promotes mental health support through confidential programs like Healthy Minds.

• Compensation packages include competitive base pay and performance-based bonus incentives.

• Retirement planning resources, financial counseling, and savings tools help employees achieve long-term security.

• All employees are welcomed into a culture of openness, respect, and inclusion, with numerous employee resource groups and community engagement platforms.

• American Express complies with local employment laws and ensures fair, bias-free hiring practices.

• Offers are subject to background verification as per regional guidelines and employment regulations.

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Key Responsibilities

Understanding Business Requirements

• Translate business objectives into machine learning goals and establish relevant data science frameworks.

• Engage with product owners, operations teams, and domain experts to define project scope and success metrics.

• Maintain a holistic view of organizational goals to align model development efforts accordingly.

Designing ML Solutions

• Build models using a wide array of machine learning algorithms such as decision trees, random forests, gradient boosting, and neural networks.

• Tailor solutions to improve internal servicing operations and customer satisfaction.

• Experiment with different data preprocessing, feature engineering, and selection methods to optimize performance.

Natural Language Processing (NLP) Focus

• Work extensively with textual data across multiple sources such as chat logs, emails, and survey responses.

• Apply and enhance NLP techniques like named entity recognition, sentiment analysis, and topic modeling.

• Utilize pre-trained models and LLMs for customized language understanding applications.

Data Engineering and Wrangling

• Clean, transform, and prepare data for analysis, ensuring consistency and reliability across different sources.

• Create robust ETL pipelines to feed data into ML models and dashboards.

• Identify missing values, outliers, and anomalies through thorough exploratory data analysis.

Coding and Debugging

• Write and maintain high-quality code in Python for data analysis, model building, and automation.

• Debug and optimize code to ensure reliability, efficiency, and scalability in deployment.

• Collaborate with DevOps and engineering teams for model deployment into live systems.

Model Evaluation and Testing

• Establish evaluation frameworks using metrics such as accuracy, precision, recall, ROC-AUC, and F1-score.

• Conduct A/B testing, cross-validation, and performance benchmarking against baseline models.

• Iterate and improve models based on feedback and continuous monitoring post-deployment.

Research and Innovation

• Stay current with developments in AI and ML through continuous reading, courses, and participation in communities.

• Propose innovative techniques and methodologies that can lead to breakthrough results in business processes.

• Explore opportunities to patent original work or present research in internal and external forums.

Team Collaboration

• Work closely with peers from engineering, analytics, and business teams.

• Contribute to team goals, provide mentorship to junior analysts, and review code or project documents.

• Maintain clear and proactive communication with all team members and stakeholders.

Project Documentation

• Document all aspects of model development, from data sourcing to model deployment, for future reference and audits.

• Create user guides, training materials, and dashboards for internal stakeholders.

• Maintain a knowledge repository of techniques, models, and performance metrics.

Please click here to apply.

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