{"id":3851459,"url":"https://alion.io/job/astrazeneca-senior-engineer-process-data-science-and-statistics","title":"Senior Engineer, Process Data Science and Statistics","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":94,"open_postings":171,"ghost_share":0.006,"stale_share":0.374,"repost_share":0.099,"time_to_fill_p50_days":19,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gaithersburg, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":101643,"max":152464,"currency":"USD","period":"year","gross":null,"usd_annual":152464},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Plotly","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"AWS","optional":true},{"name":"Databricks","optional":true},{"name":"LLM","optional":true},{"name":"Snowflake","optional":true},{"name":"Statistical Process Control","optional":true},{"name":"Streamlit","optional":true}],"status":"live","first_seen_at":"2026-11-05T00:00:00Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T08:46:21Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"The Sr Engineer will develop and maintain analytical workflows for multivariate process monitoring, predictive modeling of batch outcomes from incoming material, and anomaly detection on high-dimensional process data. This role builds and maintains data systems, analytical platforms, and AI-enabled tooling that connect process characterization and manufacturing execution data to statistical analysis and decision support - scaling from early process development through late-stage clinical development and commercial and post-commercial manufacturing. The role will also support development of hybrid mechanistic and data-driven models and human-supervised agentic AI applications.\nThis position reports to the Director, Process Data Science and Statistics lead, Process Engineering, Cell Therapy Development and Operations, and is located Required Qualifications\nEducation\nPh.D. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 2+ year of relevant experience\nOR M.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 4+ years of relevant experience\nOR B.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 5+ years of relevant experience\nTechnical Skills\nDemonstrated experience in applied data science and statistical methods for process development or manufacturing (process capability, variance components, DOE, control charting, and multivariate analysis)\nProficiency in Python (pandas, scipy, statsmodels, scikit-learn, plotly) and SQL for data analysis and pipeline development\nExperience developing and evaluating predictive models, including data preparation, validation, and interpretation of model results\nExperience with software development practices including version control, automation, and reproducible analysis workflows\nAbility to prepare clear technical documentation and translate complex statistical findings into actionable insights for cross-functional audiences\nSoft Skills\nExcellent cross-functional communication and collaboration in matrixed environments\nAbility to work independently, set priorities, and deliver under time-sensitive program and regulatory timelines\nPractical problem-solving with ability to translate analytical concepts into maintainable tools and workflows\nPreferred Qualifications\nExperience working with cloud data systems (AWS, Snowflake, Databricks, or similar)\nExperience building interactive analytical applications (Streamlit, Dash, or similar)\nExperience with multivariate SPC methods, including PCA/PLS, Hotelling T-squared, SPE/DModX, and anomaly detection\nExperience with hybrid mechanistic and data-driven modeling, process simulation, digital shadows, or neural ODEs\nExperience with AI/ML methods applied to manufacturing or bioprocess problems, including incoming-material batch-outcome prediction and LLM-based or agentic tooling\nExperience in cell therapy or biologics manufacturing\nExperience with DOE design and analysis for process characterization\nTrack record of building and deploying automated reporting and process monitoring systems\nAccountabilities\nStatistical Strategy & Regulatory Support\nSupport statistical analysis for comparability assessments (site transfers, significant process changes), process characterization, and control strategy development for cell therapy programs\nApply rigorous statistical frameworks (equivalence testing, process capability analysis, variance component analysis, tolerance intervals) to evaluate drug product quality and support defensible regulatory narratives under tight timelines\nDesign and analyze DOE studies for process characterization and LVV manufacturing, including risk-based parameter selection and interaction modeling\nSupport process and product characterization for early development activities\nProcess Monitoring & Reporting\nDevelop and maintain process monitoring workflows for pivotal and commercial manufacturing, including statistical control charts, alert/action limits, and trend detection\nBuild multivariate statistical process control capabilities (e.g., principal component analysis or partial least squares), to support continued process verification, batch review, and investigations\nDevelop and evaluate anomaly detection methods for high-dimensional process data to support investigation triage and batch review\nBuild structured interactive reports and dashboards delivering batch visibility, in-process trend tracking, and decision support across programs\nDevelop predictive models linking incoming material attributes to batch outcomes to support assessment ahead of manufacturing run commitment\nData Infrastructure, Digital Systems and AI/ML\nPartner with IT to implement data pipelines and analytical platforms (including tools such as Snowflake, Streamlit applications, and other cloud platforms enabling automated reporting) that connect process development and manufacturing execution data to statistical analysis and decision support\nSupport manufacturing data digitization, working with IT and manufacturing sites to establish scalable data capture and integration for commercial readiness\nDevelop and evaluate hybrid mechanistic and data-driven models, including digital shadows and, where appropriate, for process understanding, forecasting, and decision support\nBuild and maintain reproducible AI/ML workflows, including data preparation, model evaluation, documentation, and monitoring, with emphasis on explainability and traceability\nDevelop constrained, human-supervised agentic AI applications for knowledge retrieval, investigation and deviation triage, digital batch-record assistance, scheduling support, and guided decision support\nTechnical Leadership & Mentorship\nContribute to digital manufacturing and data science capability development, supporting AI/ML integration and scalable infrastructure to enable commercial readiness\nTranslate process and manufacturing questions into practical analytical solutions, prioritizing multivariate process monitoring and incoming-material predictive modeling while supporting evaluation of emerging methods\nShare statistical methods, reusable code, and applied data science practices with scientists and engineers to elevate organizational capability\nCollaborate cross-functionally with R&D, MSAT, Manufacturing Operations, Quality, and IT to align data capabilities with program needs\nRequired QualificationsEducationPh.D. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 2+ year of relevant experience\nOR M.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 4+ years of relevant experience\nOR B.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 5+ years of relevant experience\n\n Technical SkillsDemonstrated experience in applied data science and statistical methods for process development or manufacturing (process capability, variance components, DOE, control charting, and multivariate analysis)\nProficiency in Python (pandas, scipy, statsmodels, scikit-learn, plotly) and SQL for data analysis and pipeline development\nExperience developing and evaluating predictive models, including data preparation, validation, and interpretation of model results\nExperience with software development practices including version control, automation, and reproducible analysis workflows\nAbility to prepare clear technical documentation and translate complex statistical findings into actionable insights for cross-functional audiences\n\nSoft SkillsExcellent cross-functional communication and collaboration in matrixed environments\nAbility to work independently, set priorities, and deliver under time-sensitive program and regulatory timelines\nPractical problem-solving with ability to translate analytical concepts into maintainable tools and workflows\n\n Preferred QualificationsExperience working with cloud data systems (AWS, Snowflake, Databricks, or similar)\nExperience building interactive analytical applications (Streamlit, Dash, or similar)\nExperience with multivariate SPC methods, including PCA/PLS, Hotelling T-squared, SPE/DModX, and anomaly detection\nExperience with hybrid mechanistic and data-driven modeling, process simulation, digital shadows, or neural ODEs\nExperience with AI/ML methods applied to manufacturing or bioprocess problems, including incoming-material batch-outcome prediction and LLM-based or agentic tooling\nExperience in cell therapy or biologics manufacturing\nExperience with DOE design and analysis for process characterization\nTrack record of building and deploying automated reporting and process monitoring systems\n\nThe annual base pay (or hourly rate of compensation) for this position ranges from $101,643.20 - $152,464.80. Our positions offer eligibility for various incentives-an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.\nDate Posted\n05-Nov-2026Closing Date\n29-Nov-2026Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. 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