Data Scientist, Global Growth

Valtech

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 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 behaviour into clear narratives for non-technical stakeholders including compliance officers, fraud investigators, and C-suite sponsors.  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.  If you do not meet all the listed qualifications or have gaps in your experience, we still encourage you to apply. At Valtech, we recognize that talent comes in many forms, and we value diverse perspectives and a willingness to learn.  COMMITMENT TO REACHING ALL KINDS OF PEOPLE  We design experiences that work for all kinds of people - and that starts with our own teams. At Valtech, we’re intentional about building an inclusive culture where everyone feels supported to grow, thrive and achieve their goals. No matter your background, you belong here. Explore our Diversity & Inclusion site to see how we’re creating a more equitable Valtech for all.    THE BENEFITS   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.    Your application process Once you apply, our Talent Acquisition team will review your application. Your CV should cover key information on relevant experiences and expertise. We do not require information such as age, gender, marital status, or a headshot in your application. We review all candidates based on skills, experience, and potential. ⚠️ Beware of recruitment fraud! Only engage with official Valtech email addresses ending in @valtech.com. 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.    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.

Volvo Personvagnar Aktiebolag

Company description: Who are we?Volvo Cars is a company on a mission; to bring traditional car manufacturing into a connected, sustainable and smart future.Since 1927, we have been a brand known for our commitment to safety, creating innovative cars that make life less complicated for our consumers. In 2010, we decided to transform our business, resulting in a totally new generation of cars and technologies, as well as steady growth and record sales. Today, we’re expanding our global footprint in Europe, China and the US, and we’re on the lookout for new talent. We are constantly pushing our own skills and abilities to drive change in the automobile industry like never before. We are looking for innovative, committed people to join us in this endeavour and create safe, sustainable and connected cars. We believe in the power of people and will challenge and support you to reach your full potential. Join us and be part of Volvo Cars’ journey into the future. Job description: Let's introduce ourselves The Engineering Data Hub at Volvo Cars accelerates the use of data, analytics, and AI. Our cross‑functional team of data, software, and machine learning engineers combines technical expertise with a collaborative mindset to transform large‑scale vehicle data into actionable insights. We support the development of new products, functions, and services by analyzing complex data sources, building machine‑learning models, developing scalable cloud pipelines, creating intuitive visualizations, and create Generative AI products.    Our culture focuses on learning, inclusion, and achieving success together!   What you'll do As a data scientist, you will turn data into meaningful insights using analytics and machine learning techniques. You are comfortable working independently on innovative projects to require, amongst others, to design, train and evaluate models to analyze sensor signals and other engineering data.   You will collaborate closely with other data scientists and data engineers across different disciplines. You’ll contribute by sharing your knowledge and learning from others as you help evolve our methods, tools, and best practices.   This role suits someone who enjoys building, exploring, and delivering high‑quality analysis that drives real impact.  What you'll bring You have a strong quantitative background, with a Master’s in Statistics, Mathematics, Data Science, Computer Science, or a related field, along with 3+ years of experience in a data science or similar role. You have experience in all stages of data science projects: framing questions, analyzing data, modeling, interpreting results, and communicating insights.   You may have experience in several of the following areas (you do not need to meet all of them):  Experience with Python and aware of software and data engineering practices for production systems  Familiar with the standard data science toolkit (e.g., pyspark, Pandas, Scipy, Numpy etc.)  Experience building data pipelines on platforms such as Databricks or Snowflake  Understanding and experience applying in statistical modeling, inference, time-series analysis, and machine learning algorithms  Understanding of CI/CD principles, particularly for data or ML pipelines  Experience with developing generative AI or LLM-based applications     Beneficial to have: Familiar with front-end development (e.g., Streamlit or React)  Experience with cloud-environments ‑ (preferably Azure)  Automotive or mechatronic domain knowledge    We value how you collaborate as much as your technical skills. You should approach problems with curiosity and creativity as well as communicate clearly with both technical and non‑technical colleagues. You would take ownership and follow through as well as open discussions and multidisciplinary teamwork.

Stripe

WHO WE ARE ABOUT STRIPE Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. ABOUT THE TEAM Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work (e.g., understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, and quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background. WHAT YOU'LL DO We're looking for a variety of Data Scientists to partner with the Product, Finance, Payments, Security, Risk, Growth, and Go-to-Market teams. You'll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As Data Scientists at Stripe, it's our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics. WHO YOU ARE We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. MINIMUM REQUIREMENTS * PhD with 3 years, MS or MA with 6 years, or BS or BA with 8 years of data science or quantitative modeling experience * Proficiency in SQL and a computing language such as Python or R * Experience in working with cross-functional teams to deliver results * Ability to communicate results clearly and a focus on driving impact * A demonstrated ability to manage and deliver on multiple projects with a high attention to detail * Strong business acumen and experience in synthesizing complex analyses into actionable recommendations * Proficiency with AI tools to accelerate model development, analysis, and coding PREFERRED QUALIFICATIONS * Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation * Experience deploying models in production and adjusting model thresholds to improve performance * Experience designing, running, and analyzing complex experiments or leveraging causal inference designs * A builder's mindset with a willingness to question assumptions and conventional wisdom * Experience with distributed tools such as Spark, Hadoop, etc. * A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)