Staff Data Scientist

**Bengaluru**

**Data Science /**

**Full-Time /**

**On-Site**

**About Simpl**:The thrill of working at a start-up that is starting to scale massively is something else. Simpl (FinTech startup of the year - 2020) was formed in 2015 by Nitya Sharma, an investment banker from Wall Street and Chaitra Chidanand, a tech executive from the Valley, when they teamed up with a very clear mission - to make money simple, so that people can live well and do amazing things. Simpl is the payment platform for the mobile-first world, and we're backed by some of the best names in fintech globally (folks who have invested in Visa, Square and Transferwise), and has Joe Saunders, Ex Chairman and CEO of Visa as a board member.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. In place of traditional forms of identification and authentication, Simpl integrates deeply into merchant apps via SDKs and APIs. This allows for more sophisticated forms of authentication that take full advantage of smartphone data and processing power.**About the role**:At Simpl, data stands as the most precious asset. Within Simpl, the Data Science Team plays a vital role in the development and maintenance of machine learning models that leverage alternative data for different stages of the credit lifecycle. These stages include approval, user behavior monitoring, and collections.As a Staff Data Scientist with a strong background in NLP, Machine Learning, and Deep Learning, you will play a critical role in shaping our data-driven solutions. You will be responsible for designing and implementing advanced algorithms and models, utilizing NLP techniques, ML methodologies, and DL frameworks to extract valuable insights from vast and complex datasets. Your expertise will contribute to enhancing our products, optimizing processes, and providing data-driven recommendations that drive business success.**Responsibilities**
- 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.
- 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.
- 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.

**Qualifications**
- A bachelor's or master's degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or a related discipline.
- Proficiency in programming languages such as Python, with a strong understanding of data manipulation, statistical analysis, and machine learning libraries and frameworks.
- Proven track record of designing and implementing NLP models and algorithms for various tasks, along with experience in handling unstructured textual data.
- Experience in working with big data technologies and cloud platforms preferably AWS.
- 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.
- 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.
- Good written and verbal communication skills, with the ability to explain complex data science concepts to both technical and non-technical audiences.