Role Description Tiger Analytics is looking for a Senior Data Scientist with a good blend of data analytics background, practical experience in Operation research strategies and Pricing Analytics within supply chains, and strong coding capabilities to add to our team. - Responsible for refactoring the Optimization algorithm written in Python using Object Oriented Programming. - Work on the latest applications of data science to solve business problems in the Supply chain and optimization space of Retail and/or CPG. - Utilize advanced statistical techniques and data science algorithms to analyze large datasets and derive actionable insights related to Pricing Optimization. - Develop and implement predictive models and optimization algorithms to improve inventory management, reduce stockouts, and optimize resource allocation across the supply chain. - Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions. - Design and execute experiments to evaluate the effectiveness of different replenishment strategies and allocation policies. - Monitor and analyze key performance indicators (KPIs) related to replenishment and supply chain allocation, and provide recommendations for continuous improvement. - Stay abreast of industry trends and best practices in data science, replenishment optimization, and supply chain management, and leverage this knowledge to drive innovation within the organization. - Collaborate, coach, and learn with a growing team of experienced Data Scientists. Qualifications - Proven experience 6+ years working as a Data Scientist, with a focus on supply chain optimization and inventory allocation. - MS or PhD in Computer Science, Operations Research, Applied Mathematics, Machine Learning, or a related field. - Experience with using mathematical programming solvers such as Gurobi, Xpress MP, CPLEX, or Google OR Tools in applications. - Experience with MLflow and model lifecycle management. - Experience building end-to-end ML pipelines in production. - Solid understanding of statistical methods, optimization techniques, and predictive modelling concepts. - Strong proficiency in programming languages such as Python, Pyspark and SQL, and experience working with data analysis and machine learning libraries. - Ability to apply various analytical models to business use cases. - Exceptional communication and collaboration skills to understand business partner needs and deliver solutions and explain to business stakeholders. Benefits This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Company Description Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce. - Market leaders in AI and analytics consulting in the CPG & retail industry. - Over 40% of revenues coming from the CPG & retail sector. - Fastest-growing sector with a focus on beefing up talent.

• Working as part of the Global DPO Office to provide practical, proportionate data protection advice, oversight and challenge across the bank. • Reviewing DPIAs, providing DPO opinions, assessing data breaches, and supporting clear, well-documented decisions on regulatory reporting. • Supporting our relationship with regulators, improving the processes behind regulatory engagement and complaints, and providing oversight of data subject rights and contact point processes. • Leading projects within the Global DPO Office at pace, and representing the team in projects led by others where data protection compliance is affected. • Owning specific goals and workstreams within the DPO Office maturity plan, working with the DPO and wider team to build practical, scalable and efficient processes, governance and ways of working. • Proactively finding opportunities to improve how we work, including through better use of data, automation, AI and other tools that help us move faster without lowering standards. • Deputising for the DPO from time to time, giving you the opportunity to build experience across DPO-level responsibilities.

Role Description We are seeking a highly skilled GenAI / Agentic AI Engineer to design, build, and deploy autonomous, LLM-powered systems that solve complex business problems at scale. This role focuses on agentic workflows, retrieval-augmented generation (RAG), tool orchestration, evaluation, and production deployment of GenAI systems. You will work at the intersection of LLMs, systems engineering, and applied ML, building intelligent agents that reason, plan, interact with tools, and operate reliably in real-world environments—particularly across regulated domains such as healthcare. Job Duties - Agentic AI & GenAI System Development: - Design, build, and deploy agentic AI systems using LLMs, tools, memory, and planning frameworks. - Implement multi-agent and single-agent workflows for autonomous task execution, decision support, and orchestration. - Develop tool-using agents (function calling, structured outputs, APIs, databases, workflows). - Retrieval-Augmented Generation (RAG): - Design and optimize RAG pipelines, including document ingestion, chunking strategies, embeddings, vector stores, and retrieval ranking. - Implement advanced retrieval techniques (hybrid search, metadata filtering, re-ranking, query rewriting). - Evaluate and tune RAG systems for accuracy, latency, grounding, and hallucination reduction. - Model Adaptation & Optimization: - Fine-tune and adapt foundation models (instruction tuning, LoRA, adapters) for domain-specific use cases. - Optimize prompts, schemas, and system instructions for reliability and determinism. - Apply reinforcement or feedback-driven optimization where applicable (human or automated eval loops). - Evaluation, Monitoring & Governance: - Define evaluation frameworks for GenAI systems, including task success, factuality, grounding, latency, and cost. - Build monitoring and observability for agent behavior, tool calls, and failure modes. - Partner with governance and risk teams to ensure responsible AI practices, traceability, and compliance. - Production Deployment & MLOps for GenAI: - Deploy GenAI and agentic systems into production using cloud-native architectures. - Implement CI/CD, versioning, rollback, and runtime safeguards for LLM applications. - Optimize systems for performance, cost efficiency, and scalability. - Collaboration & Leadership: - Collaborate closely with software engineers, product managers, data scientists, and business stakeholders. - Translate ambiguous business problems into well-structured agentic solutions. - Mentor junior engineers and contribute to GenAI best practices and internal standards. Qualifications - Strong Python proficiency and experience building production-grade services. - Deep understanding of LLMs and foundation models (GPT, Claude, Llama, etc.). - Hands-on experience with agent frameworks (e.g., LangGraph, Semantic Kernel, DSPy, AutoGen, CrewAI, custom frameworks). - Strong knowledge of RAG architectures, vector databases, and embedding models. - Experience with structured outputs, function calling, JSON schemas, and tool orchestration. - Familiarity with LLM evaluation techniques and failure mode analysis. - Experience with APIs, microservices, and distributed systems. Requirements - Master’s Degree in Computer Science, Data Science, Statistics, or a related field. - 6+ years’ work experience as a data scientist preferably in healthcare environment but candidates with suitable experience in other industries will be considered. - Knowledge of big data technologies (e.g., Hadoop, Spark). - Familiar with relational database concepts, and SDLC concepts. - Demonstrate critical thinking and the ability to bring order to unstructured problems. - Technical Proficiency: Strong programming skills in languages such as Python and R, and experience with machine learning frameworks like TensorFlow, Keras, or PyTorch. - Statistical Analysis: Excellent understanding of statistical methods and machine learning algorithms, including k-NN, Naive Bayes, SVM, and neural networks. - Experience with Agentic Workflows: Familiarity with designing and implementing agentic workflows that leverage AI agents for autonomous operations. - RAG Techniques: Knowledge of retrieval-augmented generation techniques and their application in enhancing AI model outputs. - Model Fine-Tuning Expertise: Proven experience in fine-tuning models for specific tasks, ensuring they meet the required performance metrics. - Data Visualization: Proficiency in data visualization tools (e.g., Tableau, Power BI) to present complex data insights effectively. - Database Management: Experience with SQL and NoSQL databases, data warehousing, and ETL processes. - Problem-Solving Skills: Strong analytical and problem-solving abilities, with a focus on developing innovative solutions to complex challenges. Preferred Education - PHD or additional experience. Preferred Experience - Experience with cloud platforms (e.g., Databricks, Snowflake, Azure AI Studio etc.) for working with AI workflows and deploying models. - Familiarity with natural language processing (NLP) and computer vision techniques. Benefits Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V.

• Management of data management vendors providing consistent team direction/guidance and monitoring quality of work through vendor audits and key performance metrics. • Development and maintenance of program level DM deliverable timelines in accordance with program development strategy. Actively drives internal and external timeline negotiations, as needed. • Represent data management at cross-functional team meetings providing accurate study status updates and proactive communication/escalation of data management issues and risks. • Facilitate cross-functional sponsor review meetings of eCRF design and edit check specifications. • Facilitate and perform cross-functional sponsor EDC user acceptance testing. • Facilitate the development of data transfer agreements with central/specialty labs and other external data vendors. Ensuring data quality from data capture to reporting. • Facilitate the development and implementation of key data and metrics reports/listings. • Perform sponsor review of DM essential documents including annotated CRFs, eCRF Completion Guidelines, Data Management Plans and DB Go Live and Lock documentation.