AI Engineer

Job Title: AI Analytics Engineer Location: Seattle, WA - 50% onsite Schedule: Standard Working Hours Type: 1 year contract - possible extension Responsibilities • Design and build scalable data pipelines using python, SQL and cloud-based tools (Azure, AWS) • Develop and maintain analytics models and data transformation using dbt, airflow and data warehouses (ex: Snowflake, BigQuery) • Build and manage dashboards and data visualizations using web-based tools, Tableau, Power BI • Ensure data quality, governance and security compliance across engineering workflows • Responsible for ingestion, integration and delivery of data products and insights across multiple platforms applying and maintaining data integrity and governance rules • Knowledge of deep learning methods for NLP (quantitative area of study, Comp Science preferred) • Support adhoc analytics initiatives for clinical trials, commercial ops and digital health systems • Utilizes supervised or unsupervised methods, learning from vast amounts of unlabeled data to drive insight • Experience working with unstructured text • Develop high quality analytical and statistical models, insights, patterns, visualizations, that can be used to improve decision making in manufacturing operations • Responsible for documentation of all technical work both within and outside of formal document management systems Required Qualifications • 5+ Yrs of experience in data engineering, analytics or applied machine learning • Strong python and SQL Skills, experience in ETL Orchestration tools (Airflow, Prefect. Domino) ability to manipulate and analyze complex datasets. • Proficiency in modern data stack (dbt, snowflake/Big Query, version control, CI/CD pipelines) • Experience and familiarity in DataOps/MLOps frameworks, AI/LLM tooling such as OpenAI ,Langchain, Hugging face Transformers • Strong communication and stakeholder engagement skills • Experience in regulated industry (Pharma, biotech or life sciences) is highly preferred • Solid grasp of data science fundamentals: causal inference, regression, classification, experimental design Nice to Have • Knowledge of GxP, HIPAA or compliance standards • experience with data modelling 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 Per manager, the Top 3 – 5 Must Have’s a candidate should have are: • Experience in Data engineering, analytics or applied machine learning • Strong python and SQL Skills, experience in ETL Orchestration tools (Airflow, Prefect. Domino) ability to manipulate and analyze complex datasets.