Data Scientist

  Why Valtech? We’re the experience innovation company - a trusted partner to the world’s most recognized brands. To our people 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)  Role 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 behavioural 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 behavioural 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 transcri...