Credit Saison - Staff/Principal Data Scientist - R&D

About Credit Saison:

Neo-Lending Conglomerate here to enable India's credit growth story, Credit Saison India, emboldened by our Global MNC parent, is here to accelerate India's credit growth story: by building a Technology-Led Neo-Lending Conglomerate. Across our verticals, we offer bespoke solutions to meet the various

credit needs of Individuals, SMEs, FinTechs, and NBFCs. Credit Saison India ("CS India"), having been founded in 2018, operates under the registered name Kisetsu Saison Finance India Private Limited as an MNC subsidiary of its parent company - Credit SaisonCo. Ltd in Japan. Across our various business verticals like Wholesale Financing, Co-origination Financing, Consumer Financing, and SME Financing we look towards reaching resilient Assets Under Management ("AUM") of US1bn in record time.

More about us on https://www.creditsaison.in

Roles & Responsibilities:

- Collaborate closely with product and business teams to gain a comprehensive understanding of the challenges and opportunities within the Advanced AI landscape and develop a DS and AI use case roadmap for a problem area or capability for the business.

- Work as the data strategist, identifying and integrating new datasets that can be leveraged through our product capabilities and work closely with the engineering team to strategize and execute the development of data products.

- As a core member of the DS team, you will research, prototype, develop, deploy and scale innovative company wide data science (ML) , NLP, GenAI, Data & Analytics solutions aligned with business vision and goals.

- Enhance data collection procedures and run data exploration to understand relationships and patterns within the data, develop data visualisation to represent the relationships identified from data exploration.

- You will deploy a variety of Text Analytics and Image processing models on large-scale datasets to address various business problems leveraging advanced NLP, machine learning, or deep learning techniques.

- Collaborate closely with ML Engineers to design scalable systems and model architectures that enable real-time ML/AI services by taking ownership of ML pipelines from end to end, encompassing data pre-processing, model generation, cross-validation, and feedback sharing.

- Refine and deepen understanding of the algorithmic and inferential aspects of statistical analysis. Evaluate new deep learning algorithms from latest research and develop intuition about the problems for which they are likely to improve the state of the practice.

- Integrate and configure observability tools such as ml-flow, langsmith, langfuse etc, for monitoring and improving application performance.

Required skills & Qualifications:

- We're looking for someone with 6 ~ 10 years of experience manipulating data sets and building deep learning models, with a Bachelor's/Master's/PhD degree in Statistics, Mathematics, Computer Science or another quantitative field, from any of the top-tier colleges.

- Experience in mentoring junior team members, and guiding them on machine learning and advanced AI applications

- Strong problem solving skills with an emphasis on product development Experience in managing the complete life cycle of data science projects from model development to production deployment

- Excellent written and verbal communication skills for coordinating across teams.

- Good applied statistics skills such as distributions, statistical testing, regression.

- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, artificial neural networks and their real-world advantages or drawbacks.

- Familiar with commonly used NLP, machine learning, and deep learning libraries such as Tensorflow, MXNet, Caffe, PyTorch, Keras, SKLearn, Transformers, SparkNLP, Gensim, SpaCy, NLTK, Lang-chain and GPT (or similar models) and so-forth

- Experience with NoSQL databases such as MongoDB, Cassandra, HBase is desired.

- Experience with distributed data/computing tools like Map/Reduce, Hadoop, Hive, Spark is a big plus.

- Working knowledge of version control (git), and continuous integration/deployment (CI/CD). RESTful APIs and Dockers are a plus.

- Working experience in services offered by cloud technologies (AWS preferable)