Specialist, Analytics and Integrations

DescriptionResponsible for optimizing data analysis and integration across the organization, the Specialist will utilize data visualization tools like Tableau to create insightful dashboards and reports. Also work with enterprise business systems such as Salesforce (CRM) and NetSuite (ERP) to leverage data structures for analytics; familiarity with these systems or comparable systems is preferred. This role requires experience with integration platforms like Workato and with programming languages such as Python or R for custom data analysis, scripting, and task automation.The Specialist will also gather functional requirements from business users across various departments, document detailed specifications for analytics and integration projects, and apply knowledge of data modeling and SQL for complex data manipulation. This role also involves using advanced statistical methods and machine learning algorithms to analyze complex datasets and predict trends. Familiarity with cloud computing services (AWS, Azure, Google Cloud) and project management skills in agile methodologies, particularly using Jira, are essential for adapting to changing technologies and research priorities.Additionally, the Specialist will be responsible for resource allocation, workload management, project prioritization, and maintaining comprehensive project documentation. This includes coordinating testing phases to ensure solution accuracy and effectiveness. The role may also involve working with scientific datasets, understanding data governance and compliance standards, and utilizing LIMS and ELN for efficient data capture and management.Interpersonally, the Specialist must possess strong critical thinking and analytical skills, excellent communication abilities, and a proactive approach to collaborating with departments such as Sales, Marketing, Finance, R&D, and Engineering. Project management capabilities, adaptability to new technologies, attention to detail, teamwork, resilience, and a commitment to continuous learning and professional development are crucial.The Specialist may also bring innovative thinking to apply data analytics in novel ways, mentor and train team members, and exercise strong ethical judgment in handling sensitive scientific data. This position is vital in ensuring the effective integration and utilization of data analytics and AI initiatives within the organization.RequirementsProficient in data visualization and analysis tools, specifically Tableau, for creating insightful dashboards and reports.Proficiency in programming languages such as Python or R for custom data analysis, scripting, and task automation.Experienced in using integration platforms like Workato to automate workflows and connect enterprise systems.Familiarity with enterprise business systems such as Salesforce (CRM) and NetSuite (ERP) to understand and leverage data structures for analytics.Strong skills in gathering functional requirements from business users across various departments, documenting detailed specifications for analytics and integration projects.Knowledge of data modeling and SQL for complex data manipulation and extraction from various sources.Familiarity with cloud computing services (AWS, Azure, Google Cloud) for data storage, processing, and analytics solutions.Project management skills, particularly in agile methodologies, to adapt quickly to changing technologies and research priorities. Ideally proficient in using Jira for task tracking, issue tracking, and agile project management, enabling effective team collaboration and project transparency.Experience in coordinating testing phases, including unit, integration, and user acceptance testing, to ensure solution accuracy and effectiveness.Nice to Have:Knowledge of advanced statistical methods and machine learning algorithms to analyze complex datasets and predict trends.Strong skills in project prioritization techniques, capable of assessing and aligning projects with strategic business objectives and resource availability.Ability to develop and maintain comprehensive project documentation, including project plans, Gantt charts, and progress reports, ensuring stakeholders are well-informed.Skilled in risk management, identifying potential project pitfalls and proactively developing mitigation strategies to ensure project success.Ability to work with scientific and experimental datasets, understanding their unique structures and extracting meaningful insights. Examples include bioinformatics tools and databases to enhance the understanding of scientific data.Experience with Laboratory Information Management Systems (LIMS) and Electronic Lab Notebooks (ELN) for efficient data capture, management, and sharing.Knowledge of IoT (Internet of Things) technologies for lab equipment, enabling remote monitoring, process analytics, data collection, and predictive maintenance.Understanding of automation protocols and interfaces for lab equipment, ensuring integration and data flow between devices and analysis platforms.Experience in applying AI and machine learning models to biological data, enhancing research outcomes, predictive analytics, and personalized medicine approaches.Knowledge of natural language processing (NLP) techniques for mining insights from scientific literature, patents, and lab notes.Interpersonal Skills:Strong critical thinking and analytical skills to dissect complex problems and devise data-driven solutions.Excellent communication skills, with the ability to translate technical concepts into understandable terms for non-technical stakeholders.Proactive in collaborating with various departments, including Sales, Marketing, Finance, R&D, and Engineering, to understand their data and process integration needs.Adaptable and quick to learn new technologies and methodologies to stay at the forefront of data analytics and integration trends.Detail-oriented with a focus on accuracy and quality in all aspects of data analysis and reporting.A team player who thrives in a collaborative environment, willing to share knowledge and assist colleagues in cross-functional projects.Resilience and problem-solving agility, especially important in the fast-paced and often unpredictable nature of biotech research and development.Openness to continuous learning and professional development to keep up with the rapidly evolving fields of data science and biotechnology.

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