All about Zeta :
Zeta is the world’s first and only Omni Stack for banks and fintech’s. We are rethinking payments from core to the edge, led by the vision to augment the purpose of money and banking with technology. A single, modern software stack comprising processing, loans, customizable mobile and web apps, a fraud engine, and rewards for retail banking.
We are a new-age, high-growth startup (& a unicorn!) founded in 2015 by two visionary leaders, Bhavin Turakhia & Ramki Gaddipati, whose entrepreneurial legacy & excellence has put us on top of the global fintech ecosystem. Zeta counts amongst its customers over 10 banks and 25 fintech’s across 8 countries – some of our notable clients include Sodexo – a leading issuer of employee benefits & rewards with over 30 million global users, and HDFC Bank – the 14th largest bank by market cap in the world. Learn more about our manifesto & beyond.
What is the job like?
– Partner with Engineering, Product, and Operations teams to conceive, design & build AI systems for Email Suites, Conversational chatbots, and various other NLP and ML use cases.
– Develop innovative strategies and models to operationalize the solutions.
– Generate ideas for exploratory analysis to shape future projects and provide recommendations for actions.
– Rapidly prototype solutions and drive/be involved in product and feature discussions.
– Create dashboards and documentation to communicate results and monitor key data metrics regularly.
– Collaborate with software engineers to deploy and integrate data models into production systems, ensuring scalability, reliability, and efficiency.
Identify key business metrics and recommend product features if needed.
Who should apply for this role?
– Master/Bachelor’s degree in Machine Learning/Data Science, Applied Statistics, Mathematics, or Engineering.
– Minimum 8+ years of relevant experience with a proven track record of developing ML solutions.
– Strong quantitative and problem-solving skills.
– Solid understanding of NLP and knowledge of essential data science libraries, including Pandas, Numpy, Scipy, and Scikit-Learn.
– A good hands-on experience with Python used in data analysis and model building.
– Experience with Hadoop, Spark, or other distributed computing systems for large-scale training.
– Strong understanding of supervised (decision trees, random forests, boosting, etc.) and unsupervised ML techniques.
– Ability to break down and frame business problems into data science solutions and hands-on capability to create MVP out of it and run a DS project end to end independently
– Communication skills to engage with Business stakeholders to understand their needs and effectively communicate the results of the analytical solution
– Able to work effectively in a Team.
– Ability to write clear and concise technical documentation
Good to have skills
– Knowledge of experimental design and working with skewed datasets.
– Prior work in any of the following areas: Recommendation Systems, Chatbots, -Summarization, etc.
– Knowledge of database systems, SQL, and NoSQL databases.