Role Overview The Senior Data Scientist will be responsible for designing and developing advanced predictive modeling, automated decisioning, and intelligent document processing modules for our enterprise software platform.

An ideal candidate will have 6+ years of hands-on software development and data science experience, primarily within the financial sector (banking, risk, or fraud). They must demonstrate deep expertise in building scalable pipelines capable of handling variety of structured and unstructured data. As a key member of the Product Development team, this individual will report directly to the Chief Data Scientist and balance their time across core product development, cutting-edge AI research, and high-impact client deployments.

We are looking for a highly technical self-learner who thrives on architectural challenges, stays at the forefront of the AI landscape, and writes production-grade code.

Key Responsibilities

Core Product Development & Advanced Modeling

· Multi-Paradigm Engine Expansion: Drive the product vision to expand our current modeling and decisioning capabilities to other use cases and industries

·Feature Engineering & Data Prep: Architect end-to-end data preparation, advanced feature engineering, and robust validation pipelines optimized for diverse, large-scale datasets.

·Production-Grade Software: Translate complex algorithmic approaches into clean, maintainable, object-oriented, and test-driven production code.

MLOps, Research, & Client Deployment

· MLOps Integration: Deploy, monitor, and maintain predictive models within our established MLOps framework, ensuring model reliability, scalability, and zero-downtime integration.

· Client Deployment & Consultation: Partner closely with enterprise financial institutions during client deployment activities, translating custom tenant data requirements into scalable platform modules.

· Rapid Prototyping: Build MVPs and Proof of Concepts (POCs) to benchmark new algorithms, public cloud services, and emerging AI tools.

GenAI, RAG, & Intelligent Document Processing





· Structured Data Extraction: Design, implement, and optimize Generative AI and Retrieval-Augmented Generation (RAG) pipelines to extract hyper-accurate structured data from unstructured financial documents (e.g., invoices, bank statements, tax returns, and financial statements).

· Agentic Workflows: Research, prototype, and build intelligent, multi-agent AI workflows integrated into a no-code interface, enabling automated, complex decisioning pipelines.

· GenAI Supporting Tools: Integrate LLMs as supporting mechanisms to augment traditional ML data preparation, documentation, and model evaluation workflows.

Requirements : Skills & Knowledge

Experience & Education

· Education: A Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Quantitative Finance, or a related technical field.

· Experience: 6+ years of professional industry experience in a software development or data science role involving machine learning, data engineering, and generative AI pipelines.

· Domain Expertise: Good to have -Proven track record in Financial Services (credit risk modeling, fraud, decisioning, or banking analytics).

Technical Skillset

· 100% Hands-on Coding: Exceptional, production-level proficiency in Python and standard object-oriented programming/TDD clean-code practices.

· Traditional Machine Learning: Expert-level knowledge of supervised and unsupervised ML algorithms (e.g., Tree-based ensembles like XGBoost/LightGBM, regression models, distance-based algorithms, and time-series frameworks).

· Generative AI Engineering: Hands-on experience building, fine-tuning, and evaluating LLM pipelines, RAG systems, and semantic parsing frameworks using modern tools (e.g., LangChain, LlamaIndex, vLLM, or vector databases).

· Data & Cloud Infrastructure: Experience working with large-scale data engineering workflows, parallel processing frameworks, and cloud environments (AWS, Azure, or GCP).

· Explainable AI (XAI) & Fairness (Plus): Familiarity with Model Explainability frameworks (e.g., SHAP, LIME) and algorithmic bias mitigation techniques in a regulated setting is a strong plus.

📌 Senior Data Scientist (Hyderabad)
🏢 finbotsAI
📍 Hyderabad