Job Description
BENGALURU /
DATA SCIENCE /
FULL-TIME
/ ON-SITE
About Simpl:
Simpl is India’s leading checkout network meant to make payments invisible and money intelligent. It is committed to the simplification and democratization of digital transformation in the payments space.
As India’s foremost consumer experience platform, Simpl is on a mission to empower merchants to build trusted relationships with customers, one transaction at a time. With more than 20,000+ available merchants and millions of trusted users pan-India, Simpl envisions creating an inclusive digital payment experience for India that empowers and fosters trust between merchants and their customers.
Simpl proves a full-stack solution for e-commerce conversion. It enables merchants to give customers 1-click checkout, buyer protection, and a pay-later facility to allow them to feel safe and trusted when shopping online. Through Simpl, merchants can provide consumers with an easy, secure, and intuitive user experience.
Everyone at Simpl is an internal entrepreneur who is given a lot of bandwidth and resources to create the next breakthrough towards the long term vision of “making money Simpl”. Our first product is a payment platform that lets people buy instantly, anywhere online, and pay later. In the background, Simpl uses big data for credit underwriting, risk and fraud modeling, all without any paperwork, and enables Banks and Non-Bank Financial Companies to access a whole new consumer market.
About the role:
Data is the most valuable asset at Simpl. The Data Science Team is a key component of Simpl, responsible for developing and maintaining ML models, using alternative data, for various stages of the credit lifecycle: approval, user behavior monitoring and collections.
Responsibilities
1)Design, develop, and deploy machine learning models and algorithms for various applications such as classification, regression and clustering. Utilize deep learning techniques and frameworks to solve complex problems.
2) Evaluate and fine-tune machine learning models to achieve optimal performance metrics. Conduct experiments and A/B testing to improve model performance and explore different algorithmic approaches.
3) Analyze complex and large-scale datasets using statistical and machine learning techniques. Develop and implement predictive models, algorithms, and statistical analyses to extract insights and drive decision-making processes.
4) Work closely with cross-functional teams, including data engineers, software developers, and business stakeholders, to understand requirements and develop data-driven solutions. Collaborate with team members to tackle complex problems and provide guidance to junior data scientists.
5) Stay up to date with the latest trends and advancements in the DS field and propose innovative solutions to business problems.
Qualifications
1)A bachelor's or master's degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or a related discipline.
2) 4 to 6 years of hands-on experience in data science, machine learning, and statistical analysis. Experience in applying data science techniques to real-world business problems, preferably in the financial or fintech industry.
3) Proficiency in programming languages such as Python, with a strong understanding of data manipulation, statistical analysis, and machine learning libraries and frameworks.
4) Experience in working with big data technologies and cloud platforms preferably AWS.
5) Familiarity with popular data science tools and frameworks such as scikit-learn, pandas, or Spark. Proficiency in SQL for data retrieval and manipulation from databases.
6) Experience in deep learning techniques and frameworks such as TensorFlow, PyTorch, or Keras. Experience in developing and deploying deep learning models. Ability to leverage pre-trained models and transfer learning to optimize model performance in specific use cases.
7)Good written and verbal communication skills, with the ability to explain complex data science concepts to both technical and non-technical audiences.
