A fast-growing startup is to work with life sciences customers to support the deployment of AI-driven solutions in technical and operational environments.

This is a cross-functional role that combines engineering, customer collaboration, data analysis, and technical problem-solving. You'll work with a range of stakeholders to understand operational challenges, analyze process and production data, develop models and workflows, and help drive improvements using the company's platform. If you are excited about being at the frontier of innovation in the application of AI, read on.

Internally, you'll act as a technical subject matter expert and advocate for customer needs across manufacturing and process-related initiatives.

Externally, you'll support enterprise customers by partnering with engineering and operations teams to maximize value from the platform, whether by improving existing workflows or helping implement new solutions.

In collaboration with Sales, Product, and Engineering, you'll also contribute to identifying new use cases and shaping future platform capabilities.

Responsibilities

Customer Engagement & Project Delivery

Partner with customers to support deployment and adoption of platform solutions Lead onboarding and implementation efforts across technical and operational environments Build relationships with engineering, operations, and leadership stakeholders Identify opportunities for operational improvement and data-driven insights Translate customer challenges into actionable technical solutions and product feedback Support projects from discovery through implementation and impact measurement

Technical Analysis & Problem Solving

Analyze operational and process datasets to identify trends, bottlenecks, and optimization opportunities Develop analytical models, simulations, and workflows using Python and related tools Work with industrial data systems and manufacturing datasets Collaborate with product and engineering teams to improve platform functionality Communicate technical findings to both technical and non-technical audiences Support troubleshooting and continuous improvement initiatives in complex operational settings

Qualifications

7-10 years of experience in manufacturing, process engineering, or related industrial environments, with strong understanding of biologics pharma manufacturing Experience building models from scratch, not just black-box development Experience in dynamic modelling, hybrid modelling is essential to be successful Understanding of production, pilot, or laboratory-scale operations

Required Skills

Experience using Python or similar tools for analysis, modelling, or automation

Ability to work with operational and process data Strong communication and stakeholder management skills Problem-solving mindset with experience driving technical or operational improvements Experience working in cross-functional technical teams Experience with Aspen, Python or related modelling tools for mechanistic modelling Experience in pharmaceutical manufacturing execution systems (MES)

Experience such as the below in highly relevant to the position

Bioprocess scale-up; Cell Culture Modeling Cell culture modeling CHO cell modeling Bioreactor modeling Fermentation modeling Bioprocess modeling Upstream process modeling Metabolic modeling Metabolic flux analysis (MFA) Flux balance analysis (FBA) Genome-scale metabolic models (GEMs) Media optimization modeling Nutrient consumption modeling Product quality modeling Cell growth kinetics Mechanistic process understanding