National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Bioinformatics Specialist (The Data-Driven Biologist)

Unreal Gigs
Cambridge
6 months ago
Applications closed

Related Jobs

View all jobs

Deep Learning AI Engineer / Bioinformatics - Expression of Interest

Deep Learning AI Engineer / Bioinformatics - Expression of Interest

Lecturer in Data Science in Haematology

Data Scientist

Principal / Senior Data Scientist

Data Science Manager

Are you passionate about turning complex biological data into meaningful insights that drive breakthroughs in health, medicine, and biotechnology? Do you have the skills to analyze and interpret genomic data, build computational models, and develop algorithms that unravel the mysteries of life itself? If you’re excited about using technology to transform the fields of genomics, drug discovery, and personalized medicine,our clienthas the perfect opportunity for you. We’re looking for aBioinformatics Specialist(aka The Data-Driven Biologist) to develop innovative computational tools and data-driven approaches that solve complex biological challenges.

As a Bioinformatics Specialist atour client, you’ll work closely with biologists, geneticists, and data scientists to analyze large-scale biological datasets, develop algorithms, and design software tools that aid in understanding molecular biology and genetics. Your work will drive research and development in areas like genomics, proteomics, and personalized medicine, pushing the boundaries of what’s possible in biotechnology.

Key Responsibilities:

  1. Analyze and Interpret Biological Data:
  • Analyze large-scale genomic, proteomic, and transcriptomic datasets using bioinformatics tools and software. You’ll extract meaningful insights from complex data to support research in genetics, drug development, and molecular biology.
Develop Computational Models and Algorithms:
  • Design and implement algorithms that process and interpret biological data, including sequence analysis, gene expression profiling, and protein structure prediction. You’ll create models that help predict biological functions and interactions.
Collaborate with Cross-Disciplinary Teams:
  • Work closely with molecular biologists, geneticists, and data scientists to integrate bioinformatics tools into research projects. You’ll translate biological questions into computational problems and provide solutions that drive scientific discoveries.
Build and Maintain Bioinformatics Pipelines:
  • Develop automated data analysis pipelines that handle high-throughput sequencing data, including next-generation sequencing (NGS), RNA-seq, and CRISPR screens. You’ll ensure that pipelines are scalable, efficient, and user-friendly.
Stay Up-to-Date with Advances in Bioinformatics:
  • Keep abreast of the latest developments in bioinformatics, computational biology, and data science. You’ll experiment with new tools, databases, and software to continuously improve your analytical techniques and methodologies.
Visualize and Present Data Insights:
  • Create visual representations of biological data to communicate findings to researchers, stakeholders, and non-technical audiences. You’ll use data visualization tools to illustrate complex results clearly and effectively.
Ensure Data Quality and Reproducibility:
  • Focus on ensuring the accuracy, quality, and reproducibility of bioinformatics analyses. You’ll develop validation methods and maintain data integrity to ensure that results can be trusted in scientific and clinical applications.

Requirements

Required Skills:

  • Bioinformatics and Computational Biology Expertise:Strong experience in analyzing biological datasets, including genomics, proteomics, and transcriptomics. You’re proficient in using bioinformatics tools and software like BLAST, Bioconductor, and Python or R for data analysis.
  • Algorithm Development and Data Analysis:Expertise in developing algorithms and computational models for sequence analysis, gene expression profiling, and protein structure prediction. You have a deep understanding of data structures and machine learning techniques relevant to bioinformatics.
  • Programming and Scripting Languages:Proficiency in programming languages like Python, R, Perl, or Java. You’re skilled in using these languages for data manipulation, statistical analysis, and developing custom bioinformatics tools.
  • Data Visualization:Experience with data visualization tools and libraries like Matplotlib, Seaborn, or ggplot2. You know how to create intuitive visualizations that make complex biological data easy to understand.
  • Collaboration and Communication:Strong communication skills with the ability to explain computational results to biologists, researchers, and non-technical stakeholders. You can work effectively in multidisciplinary teams, driving projects from data analysis to biological interpretation.

Educational Requirements:

  • Bachelor’s or Master’s degree in Bioinformatics, Computational Biology, Genomics, or a related field.Equivalent experience in bioinformatics and computational data analysis is highly valued.
  • A Ph.D. in Bioinformatics, Computational Biology, or related fields is a plus but not mandatory.
  • Certifications in data analysis, machine learning, or specialized bioinformatics courses are a bonus.

Experience Requirements:

  • 3+ years of experience in bioinformatics,with a proven track record of analyzing biological datasets and developing computational tools.
  • Hands-on experience with high-throughput sequencing technologies like NGS, RNA-seq, and CRISPR.
  • Experience in genomics research, drug discovery, or precision medicine is highly desirable.

Benefits

  • Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
  • Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
  • Work-Life Balance: Flexible work schedules and telecommuting options.
  • Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
  • Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
  • Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
  • Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
  • Tuition Reimbursement: Financial assistance for continuing education and professional development.
  • Community Engagement: Opportunities to participate in community service and volunteer activities.
  • Recognition Programs: Employee recognition programs to celebrate achievements and milestones.
National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.