Sr. Solutions Architect (Cloud Data, ELN, LIMS) - Europe remote

SeekUp
London
11 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Data Engineer

Senior/Lead Health Data Scientist – Statistical Genetics

Data Scientist

Data Scientist

Senior Solutions Architect · Remote Europe · Full-time

Our Client is a leader in providing advanced cloud-based solutions that transform how scientific data is managed and utilized. Dedicated to improving and extending human life, Our Client combines a cutting-edge, collaborative cloud platform with deep expertise to drive innovation and accelerate scientific breakthroughs. By enabling seamless integration of data, Our Client empowers scientists and researchers to unlock new possibilities through AI-driven insights and next-generation laboratory solutions.

Role Overview

As a Solutions Architect, you will partner with clients in the pharmaceutical and biotechnology sectors to design and deliver innovative solutions that address complex data challenges. Your role will involve collaborating with R&D teams, analyzing data environments, developing tailored strategies, and ensuring seamless integration with client systems.

This is a highly technical role that bridges the gap between business and technology, requiring you to translate complex scientific and IT requirements into impactful solutions. You will collaborate with internal teams, including sales, engineering, and product, while fostering strong relationships with external stakeholders.

Key Responsibilities

Client Engagement:

  • Work closely with laboratory teams, researchers, and IT professionals to understand workflows and challenges. Develop solutions that address their needs using Our Client’s platform.
  • Act as a trusted advisor, guiding clients through the implementation process and supporting their adoption of our tools.
  • Gather and synthesize feedback from clients to continuously enhance the platform and related solutions.

Solution Design:

  • Develop customized solutions that map technical capabilities to business objectives.
  • Provide strategic insights into how seamless data integration and AI tools can improve laboratory operations and outcomes.
  • Collaborate with internal product and engineering teams to ensure solutions align with client requirements and long-term vision.

Sales Enablement:

  • Support sales efforts by showcasing the value of Our Client’s platform through tailored presentations, product demonstrations, and consultations.
  • Develop responses to Requests for Information (RFIs), Requests for Proposals (RFPs), and Statements of Work (SOWs).
  • Identify opportunities to deepen client engagement and expand platform adoption.

Project Coordination:

  • Ensure project timelines and deliverables are met by effectively managing internal and external stakeholders.
  • Communicate technical solutions in an accessible manner, facilitating alignment among diverse teams.
  • Drive customer success by assisting with change management and ensuring smooth integration.

Skills and Expertise

Technical Proficiency:

  • Strong background in scientific data management, with a focus on life sciences research.
  • Expertise with laboratory systems, including but not limited to ELN, LIMS, CDS, and data visualization tools.
  • Knowledge of pharmaceutical R&D processes, from discovery to manufacturing. Experience with large molecule or emerging modalities is a plus.
  • Familiarity with cloud platforms and data architecture, particularly AWS, is preferred.

Soft Skills:

  • Excellent communication skills, with the ability to simplify complex technical concepts for non-technical audiences.
  • Proven ability to build relationships and collaborate effectively with diverse stakeholders.
  • Strong problem-solving and negotiation skills, with a focus on delivering value for all parties.

Business Acumen:

  • Ability to calculate ROI for proposed solutions and demonstrate the business impact of Our Client’s offerings.
  • In-depth understanding of challenges and trends within the life sciences sector.

Requirements

  • A scientific background or at least 8 years of experience in life sciences R&D IT or informatics. Experience as a bench scientist or data scientist is a significant advantage.
  • Demonstrated success in enterprise sales within the pharmaceutical industry.
  • A passion for innovation, intellectual curiosity, and a desire to thrive in a fast-paced, dynamic environment.

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.