Senior Data Scientist

About the role

This role focuses on advancing regulatory intelligence through large language models for financial services compliance. You will join a global RegTech team that delivers AI-powered SaaS solutions to simplify compliance for clients. The position operates within a fast-growth, high-performing technology department.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

General models are adapted for regulatory use cases through targeted fine-tuning and prompt optimization.

Large text and instruction-based datasets are pre-processed and cleaned to prepare high-quality training and evaluation data.

Model performance is measured and improvement opportunities are identified through systematic evaluation and testing.

Production-ready inference pipelines are built to deploy language and language models for multi-tenant SaaS applications.

Experiments with different model architectures guide decisions on task-specific fine-tuning for regulatory intelligence workloads.

Collaboration with backend and DevOps teams ensures smooth integration of language models into the existing product stack.

Staying current with advances in language model research supports ongoing refinement of regulatory compliance solutions.

Contributions may help define product requirements and use cases that shape how language models support compliance workflows.

Requirements

A strong understanding of natural language processing techniques is necessary to design and implement effective solutions.

At least 6 years of experience working as a data scientist is required for this position.

Demonstrated experience working with large text datasets is essential for handling regulatory content and instructions.

Hands-on experience with prompt engineering is required to optimize model behavior for compliance tasks.

Solid communication and collaboration skills are required to work effectively with technology and product teams.

Experience with deep learning frameworks such as TensorFlow or PyTorch is required for model development and fine-tuning.

Experience with cloud platforms such as AWS, GCP, or Azure is preferred for deployment and scalability tasks.

Practical notes

This role is based in Bangalore and requires full-time on-site engagement.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role centers on natural language processing and deep learning applied to regulatory technology.

Common tools include large language models, prompt engineering methods, and cloud-based deployment platforms.

Work happens within a diverse, globally distributed team focused on compliance innovation.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.