• Voeren van gesprekken en in kaart brengen van huidig proces rondom data registratie van de service desk • Opzetten en uitvoeren van workshops om het belang van data(kwaliteit) te verbeteren/vergroten • Evaluatiegesprekken voeren n.a.v. gegeven workshops. • Aanbevelingen doen voor de organisatie én de service desk • Opstellen van adviesrapport, met verbeterplan • Meelopen en koppelen aan een pilot waarbij nieuwe TOPdesk-module gaan inrichten als nieuw middel voor medewerkers én gebruikers om vragen te beantwoorden. • Chatbot met AI-tooling functionaliteit toetsen • Stakeholdermanagement is zeer belangrijk in deze opdracht.

Senior Data Scientist - Agentic AI HybridData ScienceFull time New York, New York, United States Overview Description Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. As a Senior Data Scientist, you will apply strong expertise in AI through the use of machine learning, data mining, and information retrieval to design, prototype, and build next-generation advanced analytics engines and services. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will develop efficient and accurate analytical models which mimic business decisions and incorporate those models into analytical data products and tools. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results. Key Responsibilities - Serve as a resource for creating AI agents and RAG databases to solve problems. Develop AI Agent tools to automate the retrieval, wrangling, and analysis of data. - Utilize combined knowledge of data structures, analytics, algorithms/models, and strong computer science fundamentals to prepare datasets, conduct analytics, and develop deployable solutions with guidance from more senior resources. - Able to analyze and prepare complex and new data sources and incorporate them into analytical solutions. - Collaborate with customers and key stakeholders on initiating ideas, performing design, analysis, and process improvement. - Develop and deploy AI and ML solutions on Google Cloud Platform. - Utilize massive data sources to craft business insights and features for innovative solutions. - Understand diverse data sources, both structured and unstructured. Requirements - - 7+ years of overall experience, 4+ years working as a GenAI Data Scientist. - Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git - Machine learning and deep learning fundamentals, natural language processing (NLP), cloud computing, multi-agent systems understanding, data analysis, programming proficiency, and a grasp of ethical considerations in AI development. - Experience with agents frameworks (preferably Langchain). - Experience with Prompt engineering- Chatbots and virtual assistants, Image generation etc. - Experience with proprietary and/or open source LLMs. - Experience in prompt engineering. - Experience with RAG and Vector Databases Benefits Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

• Business Insight & Problem Definition: Partner closely with marketing and product teams to diagnose business challenges and define appropriate data metrics, analyses, and interpretations to drive strategic decisions. • Statistical Modeling: Define requirements for and lead the creation, monitoring, and iterative improvement of key statistical models used for marketing spend optimization and product growth. • Narrative & Visualization: Design clear, unambiguous narratives and data visualizations to communicate model outputs and analysis results to broad, non-technical audiences. • Rapid Prototyping: Independently prototype and test new insights, moving from hypothesis to rigorous validation with minimal mentorship. • Technical Consultation: Reason effectively about the strengths and weaknesses of different modeling approaches, selecting and iterating on combinations that suit specific marketing contexts.

• Build models that turn raw data into actionable insights • Build lead scoring models • Develop lookalike models to identify high-fit organizations • Turn messy data into usable, structured insights • Explore predictive approaches (e.g. classification, clustering, KNN, etc.)