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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and deploying data science and machine learning solutions within the pharmaceutical domain, focusing on predictive modeling, data governance, and collaboration with multidisciplinary teams. Proficient in translating complex business challenges into actionable analytical insights and ensuring compliance with regulatory standards.
Highest-signal resume keywords
Data Science ExpertiseMachine Learning Model DevelopmentPython ProgrammingPharmaceutical Industry KnowledgeMLOps Implementation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data AnalysisPredictive ModelingStatistical AnalysisFeature EngineeringTime-Series AnalysisAnomaly DetectionClusteringRegressionOptimizationData Preprocessing
Soft Skills
Stakeholder ManagementProblem-SolvingConsulting SkillsDocumentation SkillsPresentation Skills
Tools & Technologies
PythonSQLRSASPandasNumPyScikit-learnTensorFlowAzureAWS
Industry Keywords
Pharmaceutical ManufacturingGMP/GxPData GovernanceRegulatory ComplianceHealthcare AnalyticsLife SciencesSupply Chain DataLaboratory DataQuality DataProcess Optimization
Tech Stack
Tools & technologiesAWSAzureERPETLGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Design, develop, validate, and deploy data science, machine learning, and advanced analytics solutions for pharmaceutical business and manufacturing challenges
- Analyze large, complex datasets to identify patterns, trends, anomalies, and actionable insights
- Develop predictive and prescriptive models, forecasting solutions, optimization models, and statistical analyses
- Perform data preprocessing, feature engineering, model development, evaluation, validation, and performance monitoring
- Collaborate with pharma business, manufacturing, quality, engineering, IT, data engineering, and subject-matter experts to translate business problems into analytical solutions
- Develop analytics solutions for manufacturing performance, process optimization, quality, deviation analysis, predictive maintenance, supply chain, and operational excellence
- Build reusable analytical pipelines and work with data engineering teams to establish reliable data pipelines and datasets
- Apply supervised and unsupervised learning, time-series analysis, anomaly detection, clustering, classification, regression, and optimization
- Evaluate opportunities for Generative AI and Large Language Models where appropriate
- Develop dashboards, visualizations, and data-driven insights for operational and management decision-making
- Support model deployment and operationalization through MLOps, versioning, monitoring, testing, and model governance
- Ensure regulated-data solutions follow data governance, security, privacy, validation, and regulatory requirements
- Communicate complex analytical findings and recommendations to technical and non-technical stakeholders
- Perform technical reviews and guide junior data scientists and analytics team members
Requirements
What you’ll need- 6+ years of professional experience in Data Science, Machine Learning, AI, Advanced Analytics, or related areas
- Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, Bioinformatics, or a related discipline
- Demonstrated experience applying data science within the pharmaceutical, biotechnology, healthcare, or life sciences domain
- Strong hands-on experience developing statistical and machine-learning models using real-world datasets
- Strong programming experience with Python and SQL; experience with R and/or SAS is an advantage
- Experience working with structured and unstructured datasets and performing data preparation, feature engineering, statistical analysis, and model evaluation
- Understanding of pharmaceutical processes and regulated data environments
- Experience collaborating with multidisciplinary business, scientific, engineering, and technology teams
- Experience taking data-science solutions from proof of concept through production deployment is preferred
- Experience with pharmaceutical manufacturing/MES data, process data, quality data, laboratory data, or supply-chain data is highly desirable
- Knowledge of machine learning, statistical modeling, predictive and prescriptive analytics, time-series analysis, forecasting, anomaly detection, classification, regression, clustering, optimization, feature engineering, model evaluation and validation
- Knowledge of Generative AI/LLM concepts
- Experience with Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch
- Knowledge of ETL/data pipeline and API integration concepts
- Experience with Azure, AWS, or GCP; Databricks; MLflow or equivalent MLOps tooling; Git/version control; CI/CD concepts; model deployment and monitoring; model performance and drift monitoring
- Knowledge of pharmaceutical manufacturing processes, GMP/GxP concepts, data integrity, quality and process analytics, manufacturing/MES data, laboratory/LIMS data, ERP/supply-chain data, process historian/equipment data, and regulatory/data-governance requirements
- Strong analytical and problem-solving capability
- Ability to translate business problems into data science use cases
- Strong stakeholder-management and consulting skills
- Excellent documentation and presentation skills
- Ability to explain complex AI/ML concepts to business stakeholders
- Ability to independently lead data science workstreams
- Experience mentoring junior data scientists
Benefits
Comp & perks- Flexible environment
- Diverse and inclusive culture of globally connected teams
- Health and wellness packages
- Rewards
- Learning opportunities
- Professional development and continuous learning opportunities
- Opportunities to build new skills and take on leadership roles
- Mentorship and opportunities to connect and grow
- Disability-related recruitment adjustments or accommodations
- Equal opportunity employment and commitment to Diversity, Equity & Inclusion