About Us

slice, feel easy with money.

slice’s purpose is to make the world better at using money and time, with a major focus on providing the best consumer experience. We believe that the best product will always transcend customer demographics, like how great music touches most of us. slice app brings a fast and simple way to make payments and access credit with its cornerstone products: slice account, slice UPI, and slice borrow.

At slice, you’ll have the opportunity to make a significant and positive impact on people's lives with your career. Today, we have employees with an average age of 26, sharing the same belief in innovation, self-motivation, and happiness. We tailor our working experience with the belief that the present moment is the only real thing in life. And we have harmony in the present the most when we feel happy and successful together.

We are also backed by leading investors such as Tiger Global, Insight Partners, Advent International, Blume Ventures, and Gunosy Capital.

About the role

You will work with slice's data science team. We're currently a team of 22. As a typical startup, we have classic 0-1 problems to solve. We have problems galore, and want folks who can roll up their sleeves and are up for a challenge. As a Data Scientist Intern, you will be a part of the central Data Science team. The team builds Data science solutions for all verticals within the slice (Acquisitions, Collections, Portfolio, CX, Fraud, UPI) to drive the best customer experience for slicers. As a member of our data science team, you will play a critical role in helping our organisation leverage data to make better decisions and drive strategic growth. You will be solving complex problems and driving meaningful impact through data-driven decision-making

Data Scientists help shape slice's credit and business strategies by enabling analysis-ready data. As a key member of the team, you work closely with broader Data Science team to build data powered solutions and work on various R&D initiatives.

What You will do

• End-to-end data science activities such as data wrangling, feature generation, building and evaluating predictive models.
• Work with conventional and unconventional data sources to enable informed business decisions
• Cross-team collaboration to understand the requirements and to provide support in putting the models in production
• Provide post deployment support to the teams to monitor the model performance and make changes as required

Qualifications

• Final year students in Bachelors or Master’s degree in a quantitative discipline - Statistics, Operations Research, Computer Science, Engineering, etc
• Experience with scripting language (Python preferred). Basic experience with Python libraries such as numpy, pandas, scikit-learn, etc
• Prior internship experience is a plus
• Understanding of supervised learning and unsupervised learning techniques such as scorecards, linear/ logistic regressions, random forest, neural networks, PCA, k-means clustering, etc.
• Design thinking approach to problem-solving, able to develop simple yet impactful solutions
• Exceptional aptitude, ability to learn new concepts and experiment with new methodologies.
• Strong ownership and leadership skills, excellent communication and people skills
• Action-oriented with a bias for driving results in a fast-paced environment.

Life at slice

Life so good, you’d think we’re kidding:

• Competitive salaries. Period. 

• An extensive medical insurance that looks out for our employees & their dependants. We’ll love you and take care of you, our promise. 

• Flexible working hours. Just don’t call us at 3AM, we like our sleep schedule. 

• Tailored vacation & leave policies so that you enjoy every important moment in your life.    

• A reward system that celebrates hard work and milestones throughout the year. Expect a gift coming your way anytime you kill it here.  

• Learning and upskilling opportunities. Seriously, not kidding. 

• Good food, games, and a cool office to make you feel like home. An environment so good, you’ll forget the term “colleagues can’t be your friends”.