Data Science Lead (NLP & GenAI) Summary We are seeking a highly experienced and innovative Data Science Lead with 8+ years of expertise in core data science concepts and around 2+ years of focused, hands-on experience in Natural Language Processing (NLP) and Generative AI (GenAI). You will lead strategic AI/ML initiatives, mentor junior data scientists, and deliver intelligent solutions that drive business value using both classical and modern machine learning techniques. Key Responsibilities Lead end-to-end design and delivery of data science solutions, from problem definition to deployment. Design, build, and fine-tune NLP and GenAI models for tasks such as summarization, classification, question answering, translation, and chatbot applications. Apply statistical modeling, predictive analytics, and machine learning algorithms on structured and unstructured datasets. Collaborate with product, engineering, and business teams to translate high-level business problems into data science solutions. Ensure scalability, reproducibility, and performance optimization in all machine learning workflows. Work with large-scale data processing tools and frameworks in cloud-based environments. Mentor and review work of junior data scientists and collaborate on research and experimentation. Track advancements in GenAI, LLMs, and NLP frameworks and bring innovation to enterprise AI use cases. Mandatory Skills Python: Strong proficiency in Python for data science, modeling, and scripting Machine Learning: Hands-on with classical and ensemble models (e.g., Random Forest, XGBoost) NLP (2+ years): Experience with transformers, tokenization, embeddings, sentiment analysis GenAI & LLMs: Working with GPT-like models, fine-tuning, prompt engineering Deep Learning (PyTorch / TensorFlow): Building and training deep learning models for NLP and other domains Model Deployment: Deploying models via REST APIs, Docker, or cloud-native services SQL & Data Manipulation: Strong ability to query, clean, and process data Statistical Analysis: Applied statistics, hypothesis testing, and A/B testing Version Control (Git): Experience using Git in collaborative environments Optional/nice-to-have skills Vector Databases: Experience with FAISS, Pinecone, or ChromaDB for semantic search RAG Architecture: Building Retrieval-Augmented Generation pipelines LLM Orchestration: LangChain, LlamaIndex, or similar frameworks Cloud Platforms (Azure/GCP/AWS): Cloud-based ML workflows, pipelines, and infrastructure MLOps: Model tracking, monitoring, CI/CD with MLflow, Kubeflow, etc. Big Data Tools: Spark, Databricks, or Hadoop ecosystem familiarity Experiment Tracking: Tools like Weights & Biases, MLflow Academic Research / Publications: Experience publishing whitepapers or research contributions Hand-on experience with Databricks, preferably Azure Databricks platform. Hand-on experience with Delta Lake, preferably Azure Databricks and ADLS Gen2 platforms. Educational Qualifications Master’s or PhD in Computer Science, Data Science, AI/ML, Statistics, or a related field. Certifications (preferred but not mandatory) Google Cloud or Azure AI Engineer / Data Scientist Associate Databricks Certified Machine Learning Professional DeepLearning.AI Generative AI certification Hugging Face Transformers certification

• Design and develop data pipelines • Make data modeling and schema design decisions • Deliver features rapidly using AI-assisted development tools • Contribute to definition of user stories • Collaborate with other members of the team on development & integration • Write unit tests and maintain high code quality

• Own product direction across analytics, data pipelines, and reporting systems • Define and execute a roadmap focused on scalability, data quality, and operational efficiency • Identify and drive modernization opportunities across legacy analytics workflows and tooling • Lead efforts to reduce manual processes, redundant validations, and siloed development • Translate complex data workflows into clear product requirements and deliverables • Partner with engineering to shape data models, ETL pipelines, and distributed processing systems • Ensure data products meet standards for accuracy, performance, and reliability • Drive improvements in data lineage, traceability, and documentation • Work closely with data engineers on PySpark, Databricks, and distributed systems design • Understand tradeoffs in data modeling, pipeline design, and performance optimization • Contribute to decisions on architecture, tooling, and platform evolution • Ensure alignment with cloud infrastructure, security, and governance requirements • Lead backlog prioritization across competing data and reporting needs • Balance short-term delivery with long-term system health • Drive coordination across engineering, analytics, and reporting teams • Ensure delivery meets timelines, quality expectations, and regulatory requirements • Identify gaps in current team structure, workflows, and ownership models • Drive integration of analytics work into core product teams to reduce silos and handoffs • Establish clear ownership across calculations, data modeling, reporting, and delivery • Improve team operating model to support predictable, scalable delivery • Build trust across complex stakeholder groups including technical and non-technical partners • Translate data concepts into clear business and program impact • Proactively manage risks related to data accuracy, delivery, and dependencies • Drive alignment across external partners and internal teams

ArchWell Health is a new, innovative healthcare provider devoted to improving the lives of our senior members. We deliver best-in-class care at comfortable, accessible neighborhood clinics where seniors can feel at home and become part of a vibrant, wellness-focused community. Our members experience greater continuity of care, as well as the comfort of knowing they will be treated with respect by people who genuinely care about them, their families, and their communities.  Duties/Responsibilities: - Build data integrations from internal and external sources to centralize data into a Data Warehouse environment. - Monitor data integration operations, data quality, troubleshoot, and resolve problems. - Profile data sources and map to target table formats. - Develop and monitor data quality processes and address problems. - Develop, unit test and system test integration components. - Create support documentation describing the functionality of the integrations. - Participating in technical design & requirements gathering meetings. - Participate in planning and implementing data integration and data migration activities. - Perform QA tests to ensure data integrity and quality. - Research data issues between source systems and the data warehouse. Required Skills/Experience: - Bachelor’s degree required; Master's degree (in data science, computer science or MIS, mathematics, engineering, or related field) preferred. - 5+ years of prior experience in Data Management / ETL / ELT / Data Warehousing - Experience in writing Data Quality routines for cleansing of data and capturing confidence score - Experience with master data management - Strong knowledge of Structured Query Language (SQL) and Transact-SQL (T-SQL) - Experience using scripting languages such as JavaScript or Python - Experience Healthcare data models, datasets, and source systems (e.g. EHR, claims, labs, etc.) - Experience with healthcare reference data (ICD, CPT etc.) - Experience with agile delivery methodologies - Data Modeling experience preferred. - Strong organizational, administrative, and analytical skills required. - Experience managing and working in cloud environments such as Amazon Web Services or Azure - Knowledge of HIPAA; ability to implement systems and processes in accordance with regulations - Excellent interpersonal communication skills, both written and verbal ArchWell Health is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to their race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected classification.