FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing AI and machine learning solutions, with a strong focus on model deployment, optimization, and data pipeline creation. Proficient in translating complex scientific and operational questions into actionable data products and analytics.
Highest-signal resume keywords
Python ProgrammingMachine Learning WorkflowsAI Application DevelopmentKnowledge GraphsMathematical Optimization
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningData PreparationModel ValidationDeep LearningPredictive AnalyticsNatural Language ProcessingAnomaly DetectionClassificationForecastingOptimization Logic
Soft Skills
CommunicationCollaborationProblem-Solving
Tools & Technologies
CI/CDAPIsGraph DatabasesCloud PlatformsVersion ControlExperiment TrackingModel Registries
Certifications & Qualifications
Master’s DegreePhD Degree
Industry Keywords
Chemical EngineeringBiochemical EngineeringBioinformaticsProcess ControlManufacturingSupply ChainBiomanufacturingAdvanced Process Control
Tech Stack
Tools & technologiesCloudPython
About the role
Key responsibilities & impact- Develop practical AI, machine learning, and optimization solutions for technical and operational teams
- Apply large language models, foundation models, and multimodal approaches to search, analysis, knowledge discovery, and decision support
- Build AI-enabled applications combining models, data pipelines, prompts, agents, evaluations, optimization logic, and user feedback
- Design and use knowledge graphs, ontologies, and structured domain knowledge
- Translate scientific, engineering, process control, and operational questions into data products, model workflows, optimization approaches, and user-friendly analytics
- Develop pipelines for structured and unstructured data, including time-series, laboratory, manufacturing, document, and scientific or technical knowledge sources
- Contribute to model validation, monitoring, documentation, governance, and continuous improvement
- Collaborate across data science, engineering, digital technology, operations, and business teams
- Travel regularly, potentially up to approximately 35% annually, with occasional 2–3 consecutive week on-site periods
Requirements
What you’ll need- Master’s, or PhD degree in Chemical Engineering, Biochemical Engineering, Bioinformatics, Process Control, Computer Science, Data Science or Applied Mathematics
- Demonstrated industrial experience with a proven track record of delivering measurable business impact in production or operational environments
- Hands-on experience with Python and modern machine learning workflows, including data preparation, modeling, validation, deployment, and monitoring
- Experience developing end-to-end AI or machine learning applications and deploying solutions in real industrial or production environments
- Understanding of frontier model capabilities, prompt design, agentic AI, retrieval-augmented generation, evaluation, hallucination reduction, and human-in-the-loop workflows
- Experience with structured and unstructured data, including time-series, scientific, engineering, document, knowledge base, or operational datasets
- Knowledge of mathematical optimization, process control, forecasting, anomaly detection, recommendation systems, natural language interfaces, classification, deep learning, or predictive analytics
- Familiarity with version control, testing, APIs, containers, CI/CD, and maintainable code design
- Ability to communicate model outputs, uncertainty, assumptions, control logic, optimization trade-offs, and practical implications
- Experience with knowledge graphs, ontologies, semantic modeling, graph databases, or RAG for scientific, industrial, or operational use cases
- Experience in manufacturing, process development, industrial operations, supply chain, biomanufacturing, bioinformatics, chemical processes, advanced process control, or mathematical optimization
- Experience with model registries, experiment tracking, observability, prompt and version management, evaluation frameworks, cloud platforms, or production ML systems
Benefits
Comp & perks- Remote-based working model
- Learning and development opportunities
- Collaborative environment
- Regular travel for high-impact engagement
- Exposure to diverse technical communities
- Inclusive workplace