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Knime Data Jobs in Ohio (NOW HIRING)

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

... clinical data sources. * Provide expert insights on structure-activity and structure-property ... Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger ...

Showing results 21-40

Knime Data information

What is a knime data professional?

KNIME Data professionals are specialists who use the KNIME Analytics Platform, an open-source data analytics, reporting, and integration tool. They design, build, and automate data workflows to process, analyze, and visualize data from various sources. Their responsibilities often include data preparation, data mining, machine learning, and creating interactive dashboards to support business decision-making. KNIME experts are typically skilled in data science concepts and comfortable working with databases, scripting, and advanced analytics techniques.

What are the key skills and qualifications needed to thrive as a knime data analyst?

To excel as a KNIME Data Analyst, you need strong analytical skills, a solid understanding of data science concepts, and experience with data manipulation and visualization, typically supported by a degree in computer science, statistics, or a related field. Proficiency with the KNIME Analytics Platform, knowledge of data integration tools, and familiarity with languages like Python or R are highly valued, along with relevant KNIME certifications. Strong problem-solving abilities, attention to detail, and effective communication skills help you interpret data insights and collaborate with stakeholders. These competencies are crucial for delivering actionable analytics, ensuring data accuracy, and maximizing the value of data-driven decision making.

What are some typical projects or tasks a knime data analyst might work on, and how do they collaborate with other teams?

As a KNIME Data Analyst, you will typically work on projects involving data preparation, transformation, and analysis to support business decision-making. Common tasks include building data workflows, automating reports, and developing predictive models using KNIME's visual interface. Collaboration is key—KNIME Data Analysts often work closely with data engineers to access and cleanse data, and with business stakeholders to tailor analyses to specific needs. You'll also regularly present findings to both technical and non-technical audiences, ensuring that insights are actionable and understood.

What is the difference between Knime Data vs Data Analyst?

AspectKnime DataData Analyst
Required CredentialsKnowledge of data analytics tools, certifications in data science or analyticsBachelor's degree in statistics, data science, or related field; often certifications in data analysis
Work EnvironmentData analytics platforms, data processing environments, often collaborative teamsOffice settings, reporting environments, business meetings
Industry UsageUsed within data science, analytics, and machine learning projectsApplied across industries for interpreting data, creating reports, and supporting decision-making

Knime Data refers to data processed or managed within the Knime analytics platform, focusing on data workflows and automation. Data Analysts work with data to interpret, analyze, and present insights, often using various tools including Knime. While Knime Data is about the data itself within a platform, Data Analysts utilize such data to inform business decisions. Both roles overlap in data handling but differ in scope and responsibilities.

Infographic showing various Knime Data job openings in Ohio as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Cheminformatics Specialist - Remote

micro1 AI

Cleveland, OH • Remote

$80 - $110/hr

Part-time

Posted 21 days ago


Job description

Role Title: Computational Biology & Cheminformatics Expert


Role Type: Contractor


Location: Remote


micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their expertise to a customer’s computational drug discovery project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
  2. Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank) to support AI-driven discovery platforms.
  3. Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
  4. Provide expert insights on structure-activity and structure-property relationships (SAR/SPR), medicinal chemistry approaches, and experimental design considerations.
  5. Build and implement code-based benchmark tasks (e.g., terminal/CLI-based environments) that reflect realistic computational drug discovery scenarios.
  6. Develop reproducible environments (e.g., using Docker) and automated testing pipelines to ensure task correctness and solvability.
  7. Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.


Preferred Qualifications

  1. Advanced expertise in Computational Biology, Cheminformatics, Medicinal Chemistry, Biochemistry, or related fields; advanced degree (PhD, MSc, PharmD) highly valued but not strictly required.
  2. Strong coding proficiency in Python (beyond analysis scripts), with hands-on experience building tools, pipelines, or testable code; familiarity with Git, GitHub, and Docker.
  3. Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, or MOE.
  4. Proven track record in small-molecule drug discovery, SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
  5. Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
  6. Demonstrated ability to clearly communicate complex chemical and biological concepts in written feedback and reports.
  7. Experience participating in multidisciplinary and/or remote projects; familiarity with AI-assisted coding tools is a plus.