IRIS Software Group
Sr. Artificial Intelligence - Data Analytics Engg. ( W2 or Salaried Only )
IRIS Software Group, Seattle, Washington, us, 98127
Overview
Our client, a large Pharma company, is urgently hiring a Sr. AI - Data Analytics Engineer. The role focuses on building advanced analytics infrastructure and AI-driven decision support tools for the Global Patient Operations Cell Therapy team. It blends data engineering, analytics engineering, and AI/ML integration to enhance visibility into operations such as scheduling, clinical trials, and commercial performance. The candidate should thrive in regulated environments, understand healthcare data, and own delivery end-to-end from data ingestion through to dashboarding and insight delivery. Location:
Seattle, WA Employment type:
Long Term Contract – 12 Months+ (W2 or Salaried Only) Onsite requirement:
3 days onsite every week Responsibilities
Design and build scalable data pipelines using Python, SQL and cloud-based tools (Azure, AWS). Develop and maintain analytics models and data transformations using dbt, Apache Airflow and data warehouses (e.g., Snowflake, BigQuery). Build and manage dashboards and data visualizations using web-based tools such as Tableau and Power BI. Ensure data quality, governance and security compliance across engineering workflows. Ingest, integrate and deliver data products and insights across multiple platforms, maintaining data integrity and governance rules. Apply deep learning methods for NLP and related quantitative analysis; familiarity with healthcare data is preferred. Support ad hoc analytics initiatives for clinical trials, commercial operations and digital health systems. Utilize supervised or unsupervised methods to derive insights from large datasets, including unstructured text. Develop high-quality analytical and statistical models, insights, patterns, and visualizations to support manufacturing and operations decision making. Document all technical work in appropriate document management systems. Required Qualifications
5+ years of experience in data engineering, analytics or applied machine learning. Strong Python and SQL skills; experience with ETL orchestration tools (Airflow, Prefect, Domino). Solid grasp of data science fundamentals: causal inference, regression, classification, experimental design. Ability to manipulate and analyze complex datasets. Proficiency with modern data stack (dbt, Snowflake/BigQuery, version control, CI/CD pipelines). Experience with DataOps/MLOps frameworks and AI/LLM tooling (OpenAI, LangChain, Hugging Face Transformers). Strong communication and stakeholder engagement skills. Experience in regulated industries (pharma, biotech or life sciences) is highly preferred. Nice to Have
Knowledge of GxP, HIPAA or compliance standards. Experience with data modeling in clinical trials, real-world evidence (RWE) or commercial analytics. Familiarity with NLP, time-series forecasting, or image analytics in healthcare. Master’s degree or PhD in Public Health, Economics, Statistics, Computer/Data Science, or related field.
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Our client, a large Pharma company, is urgently hiring a Sr. AI - Data Analytics Engineer. The role focuses on building advanced analytics infrastructure and AI-driven decision support tools for the Global Patient Operations Cell Therapy team. It blends data engineering, analytics engineering, and AI/ML integration to enhance visibility into operations such as scheduling, clinical trials, and commercial performance. The candidate should thrive in regulated environments, understand healthcare data, and own delivery end-to-end from data ingestion through to dashboarding and insight delivery. Location:
Seattle, WA Employment type:
Long Term Contract – 12 Months+ (W2 or Salaried Only) Onsite requirement:
3 days onsite every week Responsibilities
Design and build scalable data pipelines using Python, SQL and cloud-based tools (Azure, AWS). Develop and maintain analytics models and data transformations using dbt, Apache Airflow and data warehouses (e.g., Snowflake, BigQuery). Build and manage dashboards and data visualizations using web-based tools such as Tableau and Power BI. Ensure data quality, governance and security compliance across engineering workflows. Ingest, integrate and deliver data products and insights across multiple platforms, maintaining data integrity and governance rules. Apply deep learning methods for NLP and related quantitative analysis; familiarity with healthcare data is preferred. Support ad hoc analytics initiatives for clinical trials, commercial operations and digital health systems. Utilize supervised or unsupervised methods to derive insights from large datasets, including unstructured text. Develop high-quality analytical and statistical models, insights, patterns, and visualizations to support manufacturing and operations decision making. Document all technical work in appropriate document management systems. Required Qualifications
5+ years of experience in data engineering, analytics or applied machine learning. Strong Python and SQL skills; experience with ETL orchestration tools (Airflow, Prefect, Domino). Solid grasp of data science fundamentals: causal inference, regression, classification, experimental design. Ability to manipulate and analyze complex datasets. Proficiency with modern data stack (dbt, Snowflake/BigQuery, version control, CI/CD pipelines). Experience with DataOps/MLOps frameworks and AI/LLM tooling (OpenAI, LangChain, Hugging Face Transformers). Strong communication and stakeholder engagement skills. Experience in regulated industries (pharma, biotech or life sciences) is highly preferred. Nice to Have
Knowledge of GxP, HIPAA or compliance standards. Experience with data modeling in clinical trials, real-world evidence (RWE) or commercial analytics. Familiarity with NLP, time-series forecasting, or image analytics in healthcare. Master’s degree or PhD in Public Health, Economics, Statistics, Computer/Data Science, or related field.
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