• Analyze large volumes of customer, CRM, media, healthcare, and behavioral data to identify patterns and business drivers. • Develop measurement frameworks for pharmaceutical and healthcare marketing initiatives. • Build segmentation models, behavioral analyses, audience scoring models, attribution methodologies, and predictive analytics solutions. • Leverage Python, SQL, AI tools, and workflow automation to accelerate analytics delivery. • Translate complex analytical findings into business language and prepare executive-ready presentations. • Mentor junior analysts and promote a culture of curiosity, learning, and continuous improvement. • Act as a trusted analytics partner for Strategy, Media, and Client Services teams.

• Design, develop, validate, and improve data science models and products. • Participate in stakeholder planning sessions to gather context on ambiguous business challenges, and recommend data science modeling approaches that can drive business impact • Define customer and business value metrics, evaluation approaches, and validation criteria for models and products, including in situations with imperfect or incomplete data • Use SQL to transform complex data and build scalable datasets for modeling and analysis • Work with the machine learning engineering team to develop, validate, and deploy machine learning and AI products • Collaborate with product owners and other stakeholders to shape new data product ideas or enhancements • Develop technical design and architecture documentation for data science and AI products • Help drive technical direction, working alongside engineering and product owners to navigate tradeoffs and execution decisions • Manage priorities across multiple projects, including helping shape tradeoffs and cross-functional team priorities • Become an expert in Autodesk data - recommend approaches to make reusable features and data products that can be leveraged to ensure data science products are built on trusted, stable and scalable foundations. • Communicate analyses, model performance, methodology, and recommendations clearly to technical and non-technical audiences.

Role Description Tiger Analytics is looking for experienced Data Scientists GenAI to join our fast-growing advanced analytics consulting firm. We are seeking a highly skilled and experienced Lead Data Scientist with strong expertise in GenAI modelling. The ideal candidate will have a proven track record of designing, developing, and deploying scalable GenAI solutions, while leading projects and mentoring teams. - Work on the latest applications of data science to solve business problems - Work directly with client stakeholders to translate business problems into high level analytics solution designs - Present analytic solutions to business audiences highlighting robustness of the solution and how it could help generate business value - Develop end-to-end solutions based on in-depth understanding of business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably - Design and develop machine learning and Generative AI solutions using RAG - Build LLM-powered applications leveraging Azure OpenAI and orchestrate workflows using LangGraph - Develop agentic AI workflows for automation, insights generation, and decision support - Implement Document Intelligence solutions for extracting insights from unstructured data - Participate in discussions with team members to select and apply relevant analytic techniques and create actionable business insights - Responsible for making presentations to senior management, communicating results to business teams, and develop plans to help operationalize analytic solution Qualifications - 7+ years of experience working as a GenAI Data Scientist - Proficiency in Python and SQL - Experience with MLflow and model lifecycle management - Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git - Generative AI Knowledge: Solid understanding of latest-generation AI concepts including LLMs, prompt engineering, retrieval-augmented generation (RAG), and other contemporary generative AI applications - Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.) - Experience with Bedrock, JumpStart, HuggingFace - Experience evaluating ethical implications of AI and controlling for them (e.g., red-teaming) - Expertise in supervised learning and unsupervised learning along with experience in deep learning and transfer learning - Experience in generative algorithms (e.g., GAN, VAE, etc.) as well as pre-trained models (e.g., LLaMa, SAM, etc.) - Experience developing models from inception to deployment - 5-10 years of professional work experience with at least 5 years in Data Science - Experience building end-to-end ML pipelines in production - Familiarity with CI/CD pipelines, monitoring, and model governance - Ability to design scalable and reliable AI systems - Bachelor's in Business Analytics or equivalent work experience Benefits - Significant career development opportunities exist as the company grows - Unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment - High degree of individual responsibility Company Description Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

• Architect and continuously improve the RAG pipeline that retrieves client-specific clinical context — session notes, treatment plan goals, historical performance data — and injects it into inference-time prompts • Design the retrieval layer: chunking strategies, embedding models, vector store configuration, and retrieval ranking — optimizing for clinical relevance, not just semantic similarity • Build a context assembly system that selects and structures the most relevant clinical information for each model invocation, given token constraints and clinical priority • Evaluate retrieval quality rigorously: build test sets, measure recall and precision, and iterate on the pipeline based on where retrieval fails • Design evaluation frameworks that assess AI recommendation quality beyond standard NLP metrics — working with clinical stakeholders to define what 'good' means for each use case • Build automated evaluation pipelines that can test AI outputs at scale: LLM-as-judge evaluators, human review workflows, and clinical validity checks • Maintain evaluation datasets that reflect the real distribution of clinical scenarios the model encounters in production • Systematically identify where foundation model capabilities fall short for AnswersNow's care model: what clinical reasoning the model gets wrong, what it hallucinates, what it doesn't know how to handle • For each identified gap, recommend and implement the appropriate mitigation — improved retrieval, prompt engineering, output validation, or escalation to human review • Monitor production AI outputs for quality, drift, and failure modes using the evaluation infrastructure you've built • Define alerting thresholds and escalation paths for when AI quality falls below acceptable clinical standards • Work closely with clinical leadership and BCBAs to understand the care model deeply enough to design AI systems that support it accurately • Translate clinical domain knowledge into technical requirements: what context does the model need, what outputs are clinically acceptable, where does the model need to defer to the clinician