Computational Biologist (The Algorithmic Life Explorer)

Unreal Gigs
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

Computational Biologist / Bioinformatics Data Scientist

Bioinformatics Data Scientist - Antibody Discovery & ML

Data Engineer: Build Scalable Biotech Pipelines (Hybrid)

Principal/Senior Data Scientist

Principal/Senior Data Scientist

Senior Scientific Data Engineer, Data Platform

Are you fascinated by the idea of using algorithms and computational models to unravel the complexities of biology? Do you thrive on turning raw biological data into actionable insights that can drive discoveries in genomics, drug development, and personalized medicine? If you’re ready to harness your programming skills and deep understanding of biology to solve some of the most challenging problems in life sciences,our clienthas the perfect opportunity for you. We’re looking for aComputational Biologist(aka The Algorithmic Life Explorer) to create innovative models and analytical tools that transform how we understand biological systems.

As a Computational Biologist atour client, you’ll work at the intersection of data science, biology, and technology, developing computational techniques to interpret complex datasets. You’ll collaborate with biologists, geneticists, and software developers to design experiments, analyze biological pathways, and discover insights that have a direct impact on research and healthcare.

Key Responsibilities:

  1. Develop Computational Models of Biological Systems:
  • Create and refine computational models that simulate biological processes, such as gene expression, metabolic pathways, and protein interactions. You’ll use these models to predict cellular behavior and understand disease mechanisms.
Analyze and Interpret Genomic Data:
  • Use computational tools and techniques to analyze large-scale genomic and proteomic datasets. You’ll identify patterns, mutations, and biomarkers that are critical for drug discovery and personalized medicine.
Design Algorithms for Biological Data Analysis:
  • Develop innovative algorithms for data mining, sequence alignment, and evolutionary analysis. You’ll focus on optimizing these algorithms for speed and accuracy to handle large biological datasets efficiently.
Collaborate with Multidisciplinary Teams:
  • Work closely with biologists, data scientists, and bioinformaticians to integrate computational approaches into experimental research. You’ll bridge the gap between biological insights and computational methods to drive data-driven discoveries.
Visualize Complex Biological Data:
  • Create intuitive visual representations of computational results, making complex biological data more accessible to researchers and stakeholders. You’ll use visualization tools to convey patterns and relationships within the data clearly.
Stay Updated with Advances in Computational Biology:
  • Keep up with the latest developments in computational biology, machine learning, and bioinformatics. You’ll continuously seek out new techniques and tools to improve your analytical capabilities and methodologies.
Ensure Reproducibility and Data Integrity:
  • Focus on ensuring that computational analyses are reproducible, transparent, and rigorously validated. You’ll develop workflows that maintain data integrity and reliability for use in scientific publications and clinical applications.

Requirements

Required Skills:

  • Computational Biology and Bioinformatics Expertise:Extensive experience in computational biology, with a deep understanding of systems biology, genomics, proteomics, and transcriptomics. You’re proficient in analyzing biological data and developing computational models.
  • Algorithm Development and Programming:Strong skills in algorithm design and programming languages like Python, R, MATLAB, or Perl. You know how to develop efficient computational algorithms for biological data analysis.
  • Data Science and Statistical Analysis:Proficiency in data science techniques and statistical analysis methods for interpreting large datasets. You’re familiar with machine learning approaches applicable to biological research.
  • Data Visualization:Experience with data visualization tools such as Matplotlib, Seaborn, ggplot2, or similar. You can create compelling visualizations that highlight key insights from complex biological datasets.
  • Collaboration and Communication:Strong communication skills with the ability to explain computational concepts to non-technical researchers. You’re skilled at working in multidisciplinary teams to drive collaborative scientific efforts.

Educational Requirements:

  • Bachelor’s or Master’s degree in Computational Biology, Bioinformatics, Systems Biology, or a related field.Equivalent experience in computational modeling and biological data analysis is highly valued.
  • A Ph.D. in Computational Biology, Bioinformatics, or a similar discipline is a plus but not required.
  • Additional coursework or certifications in data science, machine learning, or bioinformatics tools are advantageous.

Experience Requirements:

  • 3+ years of experience in computational biology,with hands-on experience in developing algorithms and models for biological data analysis.
  • Proven track record in analyzing high-throughput sequencing data, gene expression datasets, or protein interaction networks.
  • Experience in research projects related to genomics, drug discovery, or systems biology 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.

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.