Data Scientist

Impact & contribution:

The Data Scientist will use technical knowledge and understanding of business domain to own and deliver moderate or highly complex data science projects independently or with minimal guidance.

You also will engage and collaborate with business stakeholders to clearly articulate findings to solve business problems.

Roles & Responsibilities:

1. Lead data-driven initiatives, from problem formulation to model deployment, leveraging advanced statistical techniques and machine learning algorithms.
2. Drive the development and implementation of scalable data solutions, ensuring accuracy and reliability of predictive models.
3. Collaborate with business stakeholders to define project goals, prioritize tasks, and deliver actionable insights.
4. Design and execute experiments to evaluate model performance and optimize algorithms for maximum efficiency.
5. Develop and deploy production-grade machine learning models in cloud-based and on-prem platforms.
6. Lead cross-functional teams in the design and execution of data science projects, ensuring alignment with business objectives.
7. Stay abreast of emerging technologies and industry trends, continuously enhancing expertise in data science methodologies and tools.
8. Drive innovation by exploring new approaches and techniques for solving complex business problems through data analysis and modelling.
9. Mentor junior team members, providing guidance on best practices and technical skills development.
10. Strongly support the adoption of data science across the organization.
11. Identify problems in the products, services and operations of the bank and solve those with innovative research driven solutions.

Essential Skills:

1. Strong hands-on programming experience in Python (mandatory), R, SQL, Hive and Spark.
2. 5+ years of experience in above skills
3. Ability to write well designed, modular and optimized code.
4. Knowledge of H2O.ai, GitHub, Big Data and ML Engineering.
5. Knowledge of Snowflake, AWS, Azure etc.
6. Knowledge of commonly used data structures and algorithms.
7. Solid foundation of Statistics and core ML algorithms at a mathematical (under the hood) level.
8. Good to have: Knowledge of Time Series, NLP and Deep Learning and Generative AI is preferred.
9. Good to have: Knowledge and hands-on experience in developing solutions with Large Language Models.
10. Must have been part of projects building and deploying predictive models in production (financial services domain preferred) involving large and complex data sets.
11. Experience in Data Science in Sales and Marketing, Campaign Analytics, Ecommerce Retail or banking products for retail or business banking is preferred.
12. Built and deployed large scale software applications.
13. Understanding of principles of software engineering.
14. Strong problem solving and critical thinking skills.
15. Curious, fast learning capability and team player attitude is a must.
16. Ability to communicate clearly and effectively.
17. Demonstrated expertise through blogposts, research, participation in competitions, speaking opportunities, patents and paper publications.
18. Most importantly - ability to identify and translate theories into real applications to solve practical problems.

Education Qualifications: Bachelor’s degree in Engineering Or Master’s degree Or Ph.D. in Data Science/ Machine Learning/ Computer Science/ Computational Linguistics/ Statistics/ Mathematics/Engineering.