Lead Data ScientistPM India

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

We are an AI Fintech company specializing in assessing credit profiles of consumers in emerging markets combining pioneering AI with large alternative data 2020 we reached our ambitious milestone of credit profiling 1bn consumers spanning 4 countries - Vietnam Indonesia India & the Philippines - and building a platform for the wider industry and the financial services industry in particular to provide the un & under served access to credit. At the core of this initiative has been our strict and unwavering adherence to the norms of consumer data privacy and consumer data rights.

But were not satisfied as we embark on the next leg of our journey to deliver 100 million credit lines to consumers in our markets. Although this goal is ambitious we truly believe that by harnessing the power of AI & Big Data we can deliver financial access at an unprecedented scale.

As a firm were audacious problem solvers motivated by our impact on society. We deeply espouse the values of ownership - of our actions and initiatives integrity in all we do and agility in execution.

We place great importance on doing what is right what is best and what is innovative. And we seek people to champion these values and beliefs as we grow.

We are seeking a Lead Data Scientist to lead the design development and deployment of machine learning systems that power critical FinTech workflows such as credit decisioning fraud prevention and intelligent document processing. This role requires deep hands-on expertise across ML modeling OCR NLP and Large Language Models (LLMs) along with the ability to make sound technical decisions and mentor junior team members.

You will work at the intersection of data science engineering and business owning end-to-end model development and ensuring solutions are scalable explainable and production-ready.

Come join our team if you are smart driven and want to make a difference in the world with the most advanced and fascinating technology. We can satisfy your desire to explore new territory and give you the runway to make big impacts.

What you will do

Key responsibilities include:

Own end-to-end ML solutions: problem framing data exploration modeling validation deployment and monitoring.
Make architectural and modeling decisions balancing accuracy latency scalability and regulatory constraints.
Review model designs and code setting best practices for the data science team.
Design and deploy advanced ML models for FinTech use cases such as credit risk fraud detection and behavioral analytics.
Lead feature engineering model optimization and rigorous offline/online evaluation.
Ensure models are interpretable fair and compliant with clear documentation for internal and external stakeholders.
Lead development of document intelligence systems including OCR layout analysis document classification and entity extraction.
Architect and implement NLP and LLM-based solutions for document understanding semantic search and automation.
Partner closely with engineering to productionize models in real-time and batch systems.
Design monitoring for model performance drift bias and data quality.
Mentor junior data scientists and provide technical guidance across projects.
Communicate complex technical concepts clearly to product risk and leadership teams.

What you will need to have

Bachelors or Masters degree in Computer Science Data Science Statistics Mathematics or a related field. Masters preferred.
6 years of experience in data science or applied machine learning with models deployed in production.
Strong expertise in machine learning statistics and data modeling.
Advanced proficiency in Python and ML frameworks (e.g. PyTorch TensorFlow scikit-learn) NLP (transformers embeddings sequence models) OCR LLMs including fine-tuning prompt engineering and RAG architectures.
Prior experience in FinTech financial services or regulated industries is a plus
Familiarity with MLOps (model versioning monitoring CI/CD feature stores).
Experience with explainability and fairness frameworks (e.g. SHAP bias testing).

At Trusting Social we deeply espouse the values of ownership integrity and agility in execution. We believe in doing what is right what is best and what is innovative. If you share our values and beliefs we invite you to join us on our journey to deliver financial access at unprecedented scale.

About us

Trusting Social an AI Fintech pioneer assessed 1bn consumers across 4 countries revolutionizing credit access. Our unwavering commitment to consumer data rights drives our journey to provide 100 million credit lines in emerging markets. With AI & Big Data we aim to deliver unprecedented financial access. As audacious problem solvers we champion ownership integrity and innovation. Join us if youre driven to impact society embracing advanced technology to make a difference. We offer a platform for exploration and impactful contributions. If youre ready to innovate and drive change come be a part of our team.

More information about us please visit our website-

Required Experience:

We place great importance on doing what is right what is best and what is innovative. And we seek people to champion these values and beliefs as we grow.

You will work at the intersection of data science engineering and business owning end-to-end model development and ensuring solutions are scalable explainable and production-ready.

What you will do

Key responsibilities include:

Own end-to-end ML solutions: problem framing data exploration modeling validation deployment and monitoring.
Make architectural and modeling decisions balancing accuracy latency scalability and regulatory constraints.
Review model designs and code setting best practices for the data science team.
Design and deploy advanced ML models for FinTech use cases such as credit risk fraud detection and behavioral analytics.
Lead feature engineering model optimization and rigorous offline/online evaluation.
Ensure models are interpretable fair and compliant with clear documentation for internal and external stakeholders.
Lead development of document intelligence systems including OCR layout analysis document classification and entity extraction.
Architect and implement NLP and LLM-based solutions for document understanding semantic search and automation.
Partner closely with engineering to productionize models in real-time and batch systems.
Design monitoring for model performance drift bias and data quality.
Mentor junior data scientists and provide technical guidance across projects.
Communicate complex technical concepts clearly to product risk and leadership teams.

What you will need to have

Bachelors or Masters degree in Computer Science Data Science Statistics Mathematics or a related field. Masters preferred.
6 years of experience in data science or applied machine learning with models deployed in production.
Strong expertise in machine learning statistics and data modeling.
Advanced proficiency in Python and ML frameworks (e.g. PyTorch TensorFlow scikit-learn) NLP (transformers embeddings sequence models) OCR LLMs including fine-tuning prompt engineering and RAG architectures.
Prior experience in FinTech financial services or regulated industries is a plus
Familiarity with MLOps (model versioning monitoring CI/CD feature stores).
Experience with explainability and fairness frameworks (e.g. SHAP bias testing).