About the Position

Data Scientist

Location: Bengaluru

At Valtech, we are the experience innovation company - a trusted partner to the world’s most recognized brands. We offer growth opportunities, a values-driven culture, international careers, and the chance to shape the future of experience.

The Opportunity

At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking, or building the next generation of customer experiences, your work will help transform industries.

We are proud of:

The work we do and the innovation we drive
Our values of share, care, and dare
A workplace culture that fosters creativity, diversity, and autonomy
Our borderless, global framework, which enables seamless collaboration

The Role

As a Data Scientist, you are passionate about experience innovation and eager to push the boundaries of what’s possible. You bring 4+ YEARS of experience, a growth mindset, and a drive to make a lasting impact.

You will thrive in this role if you are:

A curious problem solver who challenges the status quo
A collaborator who values teamwork and knowledge-sharing
Excited by the intersection of technology, creativity, and data
Experienced in Agile methodologies and consulting (a plus)

Responsibilities

Fraud & Banking Analytics

Develop, validate, and maintain supervised and unsupervised models for fraud detection, credit risk scoring, AML typology identification, and transaction anomaly detection.
Build real-time and near-real-time scoring pipelines integrating with banking event streams (Kafka, Pub/Sub) and decision engines.
Perform deep exploratory analysis of transactional data, customer behavioral signals, merchant data, and graph-based relationship networks to surface fraud patterns.
Collaborate with compliance, risk, and product teams to translate regulatory requirements (RBI guidelines, PCI-DSS, Basel III) into model design constraints.
Construct and maintain feature stores covering entity-level aggregations, velocity features, device/network signals, and geospatial behavioral attributes.
Champion model interpretability using SHAP, LIME, and counterfactual explanations to satisfy audit and regulatory scrutiny.

Generative AI & Deep Learning

Design and fine-tune LLMs (Gemini, GPT-4o, Llama, Mistral) on proprietary banking corpora using PEFT and LoRA for tasks such as SAR narrative generation, dispute summarisation, and customer communication.
Architect Retrieval-Augmented Generation (RAG) systems grounded in internal knowledge bases - policy documents, fraud rulebooks, regulatory circulars - with vector stores (Pinecone, Milvus, Weaviate, ChromaDB).
Apply Computer Vision and NLP to multimodal data pipelines (cheque images, KYC documents, audio call transcripts) for identity verification and fraud triage.
Develop generative models (GANs, VAEs, Diffusion) for synthetic data augmentation to address class imbalance in fraud datasets while meeting data-privacy obligations.
Build prompt engineering frameworks using LangChain and LlamaIndex; implement chain-of-thought and agentic reasoning for complex investigative workflows.

Engineering & Delivery

Write production-ready Python code adhering to Valtech engineering standards - unit-tested, type annotated, and reviewed.
Operationalise models with MLOps tooling (MLflow, Kubeflow, Vertex AI Pipelines) covering versioning, A/B experimentation, drift monitoring, and automated retraining.
Expose model outputs via FastAPI or Flask microservices integrated with banking middleware and case management platforms.
Work within Agile delivery squads, participate in sprint planning, demo sessions, and client-facing workshops.

Governance & Stakeholder Engagement

Enforce Responsible AI principles - bias audits, fairness metrics, model cards, and NeMo Guardrails for deployed LLMs.
Translate complex model behavior into clear narratives for non-technical stakeholders including compliance officers, fraud investigators, and C-suite sponsors.

Requirements

Must Have Qualifications

Fraud & Banking Domain

Proven track record building fraud models (card-not-present, account takeover, synthetic identity, first-party fraud, money-mule networks).
Experience with graph analytics (PyG, DGL, Neo4j) for network-based fraud ring detection.
Familiarity with banking data schemas: ISO 8583, SWIFT MT messages, core-banking extracts, and bureau data (CIBIL/Experian).
Exposure to regulatory frameworks: RBI Master Directions on Fraud, FATF AML/CFT guidelines, PCI-DSS Level 1 environments.

Core AI / ML

Expertise in supervised learning (XGBoost, LightGBM, neural networks) and unsupervised methods (isolation forest, autoencoders, DBSCAN) for anomaly detection.
Strong foundations in Computer Vision and NLP; proven experience with multimodal pipelines combining images, text, and structured tabular data.
Proficiency in PyTorch or TensorFlow for model development and custom training loops.

Generative AI Stack

Hands-on with Gemini, OpenAI GPT-4x, and open-source LLMs (Llama 3, Mistral, Phi-3).
Model fine-tuning using PEFT, LoRA, and QLoRA on domain-specific corpora.
RAG architecture design: chunking strategies, hybrid retrieval (BM25 + dense), re-ranking, and query routing.
Vector database proficiency: Pinecone, Milvus, Weaviate, or ChromaDB for semantic search and knowledge grounding.
Advanced prompt engineering: chain-of-thought, few-shot, structured output, and tool-calling patterns using LangChain or LlamaIndex.

Engineering

Python (primary): pandas, NumPy, scikit-learn, PySpark; clean, production-ready, PEP-8 compliant code with test coverage.
Cloud: hands-on experience with GCP (Vertex AI, BigQuery, Dataflow, Cloud Run), AWS (SageMaker, Redshift), or Azure (ML Studio, Synapse).
SQL proficiency for complex analytical queries across relational and columnar stores (BigQuery, Snowflake, Redshift).
MLOps fundamentals: experiment tracking (MLflow), containerisation (Docker, Kubernetes), CI/CD pipelines for model deployment.

Nice to Have Qualifications

MLOps tooling: MLflow, Kubeflow, Vertex AI Pipelines for end-to-end model lifecycle management.
Responsible AI: bias detection frameworks, model fairness metrics, NeMo Guardrails for safe LLM deployment.
API Development: wrapping models in production REST APIs using FastAPI or Flask.
Reinforcement Learning from Human Feedback (RLHF) and self-supervised learning approaches.
Experience with emerging GenAI architectures: multi-agent systems, mixture-of-experts, speculative decoding.
Databricks (Unity Catalog, Delta Live Tables, MLflow) for large-scale feature engineering and model serving.
Exposure to open-banking APIs and real-time payment rails (UPI, IMPS, RTGS) from a data perspective.

We Offer

This is a Full-Time position based in Bengaluru.

Beyond a competitive compensation package, we offer:

Flexibility, with remote and hybrid work options (country-dependent)
Career advancement, with international mobility and professional development programs
Learning and development, with access to cutting-edge tools, training, and industry experts

Our benefits are tailored to each location. Your Talent Partner will provide full details during the hiring process.

About Valtech

Valtech is the experience innovation company that exists to unlock a better way to experience the world. By blending crafts, categories, and cultures, we help brands unlock new value in an increasingly digital world.

At the intersection of data, AI, creativity, and technology, we drive transformation for leading organizations, including L’Oréal, Mars, Audi, P&G, Volkswagen Dolby, and more.

At Valtech, we don’t just talk about transformation. We make it happen. Our people are the heart of our success, and we foster a workplace where everyone has the support to thrive, grow, and innovate.

Are you ready to create what’s next? Join us.

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We are committed to inclusion and accessibility. If you need reasonable accommodations during the interview process, please either indicate it in your application or let your Talent Partner know.

Data Scientist

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