Data Science Analyst at American Express Gurugram

Key Responsibilities:

- Predictive Modeling: Develop, deploy, and validate predictive models that support profitable decision-making across risk, fraud, and marketing domains. Utilize economic logic to enhance the effectiveness of these models.

- Data Analysis: Analyze large datasets to derive actionable business insights and create innovative solutions that address complex business challenges.

- Innovation and Development: Innovate by developing and implementing new approaches using big data and machine learning techniques to enhance decision-making processes.

- Communication: Clearly articulate and structure business findings across prospect and customer domains, presenting insights and recommendations to leadership and key stakeholders.

- Collaboration: Work effectively in a team environment, integrating with cross-functional business partners across the globe to achieve common objectives.

- External Awareness: Maintain an external lens by staying informed about developments in the fields of finance, payments, analytics, and related areas.

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Minimum Qualifications:

- Educational Background: MBA or Master’s degree in Economics, Statistics, Computer Science, or related fields.

- Experience: 0-30 months of experience in analytics, big data workstreams, or related fields.

- Technical Skills: Proficiency in SAS, R, Python, Hive, Spark, and SQL. Expertise in coding, algorithms, and high-performance computing.

- Analytical Techniques: Familiarity with unsupervised and supervised machine learning techniques, including decision trees, neural models, reinforcement learning, and more.

- Problem-Solving: Demonstrated ability to solve complex problems, learn quickly, and work independently on unstructured initiatives.

- Communication Skills: Strong communication and interpersonal skills, with the ability to effectively present complex findings to both technical and non-technical audiences.

- Teamwork: Ability to work collaboratively in a team environment and contribute to cross-functional initiatives.