• 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

Role Description In our Data Science team, you'll have the chance to work as a part of our product squads delivering features that enrich our product and a first-class experience that we are known for. You'll work alongside brilliant minds, with different skills: Product Managers, Designers, UX Writers, Data Scientists, Machine Learning Engineers, Software Engineers, etc. Be prepared for the job of your life - hard challenges, high expectations, intelligent life at work, meaningful conversations, outstanding productivity. - Architect, develop and deploy machine learning systems to production. The main use cases are related to chatbots and search. - Design and execute experiments. - Analyze a wide variety of data (structured and unstructured, observational and experimental) to improve current models or create new ones. - Apply the scientific method to develop LLM-powered applications, including testing different prompts, exploring and understanding LLM capabilities, evaluating agents and prompts, assessing AI application guardrails, and building PoCs to validate the feasibility of generative AI for product features. - Participate actively in discussions and decisions regarding the whole Data Science chapter (Guilds, Design Reviews, Demos, etc.). Qualifications - People that are seeking to learn and deliver real impact through Data Science. - Expert knowledge of machine learning concepts: regression and classification, clustering, neural networks, feature selection, cross-validation, curse of dimensionality, bias-variance tradeoff, model explainability, etc. - Good understanding of the engineering challenges to deploy machine learning systems to production. - Proficiency in Python. - Some knowledge or experience with Deep Learning. - Experience with technical advice for other data scientists (technical leadership). - Excellent written and verbal technical communication skills. - Good English skills (verbal and written) is mandatory. Requirements - Have a MSc. (or Undergrad + intense experience) in machine learning, data science, information retrieval, ranking systems, recommender systems, natural language processing or other relevant fields. - Have experience with applied generative AI: AI agents, prompt engineering, LLMs finetuning (lora, qlora, peft), LLM routing, LLM monitoring, LLM guardrails. - Have experience working at fast-growing startups. Benefits - Competitive salary - Profit sharing - Meal allowance - Health insurance - Dental plan - Life insurance - Childcare subsidy and Atypical Parenthood subsidy - Wellhub - Home office allowance - Employee assistance program (mental health, social, legal, and financial support) - Extended parental leave - Day off on birthday, Mother’s Day, and Father’s Day - Benefits Club (discounts on everyday services) - Discounts at educational institutions - Reading kit for children – PlayKids