Data Scientist, MSAT

TN United Kingdom
London
6 days ago
Applications closed

Related Jobs

View all jobs

Data Scientist, MSAT

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Production

Data Scientist/Machine Learning Engineer - RNA Design

Social network you want to login/join with:

The Data Scientist will support the MSAT organisation at Orchard Therapeutics and will lead data-driven initiatives to support process and analytical control strategy development, trend monitoring, manufacturing investigations and lifecycle management of cell and gene therapy products. This role will be the primary point of contact for the development and implementation of a centralised manufacturing data repository, ensuring seamless data integration from CDMOs and internal data repositories. The job holder will drive insights that enhance process understanding, control and continuous improvement. The role will include the creation of specific processes to ensure seamless and compliant data flow and integration, data interpretation and analysis to support multiple MSAT activities linked to successful manufacture of CGT products. This role is also critical to enabling understanding of complex manufacturing processes and product characterisation.

This is an exciting opportunity to work with vector, cell process, and analytical scientists involved with Chemistry, Manufacturing, and Control (CMC) data management of gene therapy products, from early phase development to commercial production. The candidate should have experience using a variety of data mining/data analysis methods, as well as a strong statistical knowledge, DoE study experience and hands-on experience with JMP or similar software and building appropriate systems to interrogate database. Experience in working in a regulated environment is essential and the candidate must have a passion for discovering solutions hidden in large data and be comfortable working with a wide range of stakeholders and functional teams to improve process outcomes.

RequirementsRequired Experience & Knowledge:

  1. Experienced in statistical modeling, multivariate data analysis and process analytics and experienced with applications (e.g. JMP…)
  2. Proficiency using programming languages (e.g. R, Python, SQL, SAS, etc.) to manipulate data and draw insights from large data sets
  3. Proficient in DoE applied to biological processes
  4. A solid understanding of biological and bioprocessing concepts, data, and information types, experience working with CGT processes and analytical method is a plus
  5. Experience building databases compliant with requirements for regulatory filings (ICH Q8-Q10, Regulatory CPV guidance)
  6. Experience using/developing data visualization tools
  7. Knowledge of GMP data integrity, process validation (PPQ) and CPV principles
  8. Demonstrated experience applying knowledge in relevant industry fields such as Biopharma, CGT or manufacturing data analytics

Skills and Abilities

  1. Strong analytical and problem-solving skills
  2. Ability to translate complex dataset into actionable insights
  3. Ability to work in and lead diverse cross-functional teams
  4. Attention to detail and compliance mindset
  5. A drive to learn and master new technologies and techniques
  6. Excellent communication skills with an ability to visualize / present data to communicate ideas, concepts and results to technical and non-technical audiences (both internally and externally)
  7. Interest in continuous improvement of processes by integrating innovative solutions

Education

MSc or PhD in data sciences, statistics, bioprocess engineering, Bioinformatics, Engineering or related discipline.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

AI Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.