Open Positions at Genestack Limited

 

We are looking for people who want to take part in the genomics revolution to join our team. Please send a covering letter and a CV to jobs@genestack.com. We welcome applicants with strong backgrounds, even if your experience does not match our requirements exactly!

Sales Executive Cambridge, UK

Genestack is a company developing a bioinformatics platform for multi-omits R&D. We work with biopharmaceutical, consumer goods, agritech and healthcare companies and help them get the most out of their data. Our mission is to make the lives of people who do bioinformatics simpler and we do that by combining an intelligent data management system, a full range of data analysis pipelines and interactive visual tools. Our platform lowers the entry barrier to bioinformatics, so that people who don’t know how to code can still analyse and manage their data.

 

We’re a dynamic, smart and driven team led by an experienced founder, who knows the industry like the back of his hand. We’re looking for an enthusiastic Sales Executive with a proven track record to help fuel the growth of our business and participate in shaping the future of the company.

 

About the role:

 

As a Sales Executive, you would be working with our sales team on:

–Lead generation

–Nurturing of prospect relationships

–Negotiating and closing deals

–Achieving sales targets

–Ensuring customer satisfaction

–Contributing to the sales strategy development

–CRM administration

 

You will also be working with the Marketing team to closely align marketing and sales goals and objectives.

 

About you:

–You have a proven sales track record of delivering and sustaining revenue, ideally in the genomics space, but anything biotechnology-related will work too

–You are interested in genomics and bioinformatics

–You are a dynamic person, who finds it easy to network and build relationships with potential clients

–You are a hard, independent worker

–You are proactive and eager to shape the future of the company.

–You have good time management and negotiation skills

–Ideally, you have experience with CRM software (Salesforce, Hubspot) and building the sales funnel

 

 

If you are interested in this position, please send us your CV, a short covering letter explaining why do you think you would be a good fit.

Bioinformatics scientistCambridge, UK

Who are we?

Genestack is a collaborative platform for large-scale genomics data management and analysis, used by biologists and bioinformaticians from pharma, academia and biotech industries.

Our mission is to step up the pace of bioinformatics. We are tackling the underlying computational and scientific challenges of the field in order to provide researchers with tools that will streamline the discovery process, taking them swiftly from raw data to meaningful biological results.

 

The job

We’re looking for a brilliant bioinformatics scientist to join our Bioinformatics R&D team. The position will be full-time and based in our brand new office in central Cambridge.

 

Some of the things that you’d be doing:

–Investigating methods and tools at the forefront of bioinformatics research to solve   concrete scientific challenges encountered on a daily basis in our projects
–Analysing and exploring complex large-scale genomics datasets using the Genestack platform to answer practical biological questions
–Working closely with our highly talented software engineering team to embed bioinformatics pipelines into robust applications for the Genestack platform
–Implementing algorithms and building applications prototypes on the platform
 

We’re looking for candidates with the following skillset:

–Experience in bioinformatics data analysis: knowledge of data types, pipelines, databases, and typical challenges/caveats associated with bioinformatics analysis
–Scripting proficiency with Python and/or R, including data analysis libraries (e.g. Bioconductor, pandas, numpy, etc.)
–Proficiency with the UNIX command-line
–Good knowledge of statistics
–Being passionate about finding a rigorous way of investigating an ill-defined biological question
–Ability to report scientific findings
 

What we offer:

–Competitive salary
–Flexible working hours
–Relaxed working environment
–Loads of independence

 

To apply, send your CV and covering letter to jobs@genestack.com 

UI DeveloperCambridge, UK or St. Petersburg, Russia

Genestack is a genomics platform which is used by biologists to research, store, organise and process data. We have offices in St. Petersburg (Russia) and Cambridge (UK). 

 

Our front end is in HTML, CSS (Bootstrap), JavaScript (with emphasis on jQuery). Our back end is written in Java.

 

We’re searching for a UI developer in St. Petersburg or Cambridge. They will:
–Conceive, code and maintain bioinformatical and administrative UIs;
–Improve our development environment.

 

We solve hard problems within short iterations and immovable deadlines. This is why we want a strong front end specialist.

 

Our candidate is a Javascript expert. The following is required:

 

–Fundamental JS knowledge;
–Intimate knowledge of DOM APIs;
–Expertise in jQuery;
–Understanding of OOP and FP principles, and of MVC approach;
–Ability to create UI components and build UIs with them;
–Experience with unit testing;
–Experience with modern JS tooling;
–Interest in new JS trends: React, Angular 2, Webpack, etc.
–Basic knowledge of node.js.

Our candidate is also a CSS expert. The following is required:

 

–CSS fundamentals knowledge;
–CSS3 experience (incl. flexbox);
–Proficiency with browser devtools.
–Canvas and d3 experience are also very appreciated.

 

Some knowledge of Java is a plus.

 

Our UI developers design the UIs themselves. They should have some fundamental knowledge about UI design and a portfolio. We expect our candidates to have read or have knowledge of the works of Norman, Cooper, Tufte or Raskin.

 

We’re searching for a responsible and experienced developer, who is able to work on their own, without a manager (we don’t have any).

 

Contact us at jobs@genestack.com with a CV and link to portfolio.

Bioinformatics Software DeveloperCambridge, UK

Responsibilities:
  • Writing new and improving existing distributed computing analysis pipelines for large scale genomic sequencing data on- and off- the cloud
  • Developing data visualisation applications to help users interact with and extract valuable biological/clinical findings from multi-terabyte datasets
  • Working closely with our clinical, pharma, agri-tech and other customers to build apps that solve real-life complex biological data challenges and advance biomedical R&D across life sciences
Necessary Skills:
  • Knowledge of Java, Python, R or other modern programming languages
  • Experience with web-development: JavaScript and HTML/CSS
  • Ability to code for both the computational backend and web-based frontend
  • An interest in working with large-scale biological data and applications
Good to haves:
  • Any previous applied bioinformatics work especially with NGS and/or microarray data
  • Experience working with big data: orchestrating large-scale computations on the cloud, building interactive visualisations of large datasets
  • Aptitude for building good looking modern UIs, awareness of current trends in web technologies

Internship projectsCambridge, UK or St.Petersburg, Russia

Genestack is a bioinformatics company based in Cambridge. The Genestack bioinformatics platform helps to streamline big data R&D, by providing a powerful data and metadata management infrastructure, data analysis pipelines, and a range of interactive visual analytics tools for data exploration. We have users across academia, pharma, healthcare, and agricultural institutions. Our office is located within walking distance from Cambridge city centre. We are looking to take on interns for the summer with the possibility of further full-time employment.

 

All our projects start with a biological problem. You will work closely with us, receiving regular feedback and individual mentoring as you work to create a user-friendly computational solution to the problem. We expect that the work you do in your internship will have a high chance to make it into production and available to all our users.

 

Here we are presenting several project proposals. However, Genestack is evolving very rapidly and is flexible to accommodate new project ideas. So, please reach out to us, if you’d like to discuss our alternative/ upcoming projects, or should you like to propose your own project idea.

To apply, please send us a CV at jobs@genestack.com.

 

 

 

 

Single-cell RNA-seq analysis and interpretation



Aim: to explore and implement improvements to Genestack’s single-cell RNA-Seq analysis and interpretation pipeline.

 

Genestack has been developing an analysis and visualisation pipeline for single-cell RNA-Seq experiments. This includes methods to assess intercellular heterogeneity, a challenging and crucial step in single-cell RNA-Seq analysis where the amount of technical variation can be very high: we have implemented methods to identify heterogeneously expressed genes with or without spike-in data. We have also integrated visualisation and clustering methods to identify and assign cells to cell subpopulations based on the similarity of their gene expression profiles. Several dimensionality reduction techniques exist (PCA, t-SNE) coupled with automatic cluster assignment (k-means clustering).

 

Single-cell gene expression profiling is gaining wider adoption and is becoming more scalable than ever, producing single-cell datasets on with up to millions of samples. This opens up new computational challenges and bioinformatics opportunities. In this project, we will look into addressing both areas. To address the computational challenges, we’re looking to:

  • Integrate with faster expression quantification pipelines on Genestack
  • Make the visualization application scalable to handle millions of cells

In the area of bioinformatics, there are several alternatives:

  • Identify better features for subpopulations classification & analysis.
  • Look for ways to deconvolute cell cycle (using e.g. a latent variables model).

In this project, you will have the opportunity to deal with large-scale biological datasets and build powerful visualisation tools to mine them, as well as explore bioinformatics methods at the forefront of genomics and transcriptomics research.

 

 

Genetic variants analysis and interpretation

 

Aim: to explore and implement improvements to Genestack’s variants analysis and interpretation pipeline.

 

Genestack has a well-developed pipeline for WES/WGS analysis, from preprocessing and quality control, to variant calling, annotation, and association analysis integrated with external databases such as dbSNP and the 1000 Genomes Project. We’ve also built an application that lets you browse millions of variants seamlessly with real-time querying and sorting capability.

 

We’d now like to enhance this by adding more exploratory analyses capabilities and support for rare variant association testing. For exploratory analysis, we’re looking into clustering analysis of the individuals as well as clustering analysis of SNPs associated with disease (to see which variants are inherited together and if they tell us anything about pathways).

 

Our current association analysis is based only on single genetic variants, but this method is underpowered for testing rare variants, which can play key roles in influencing complex traits and diseases. To address this limitation, we’re looking to integrate SKAT, which allows for SNP-set (e.g. a gene or a region) level testing for association between a set of variants and dichotomous or quantitative phenotypes.

 

In this project, you’ll have the opportunity to analyse and explore hundreds of real patients WES datasets with multiple diseases and various phenotypes and make a contribution to discovering results of clinical significance.

 

 

Machine learning application in Bioinformatics

 

Aim: to use standard and single-cell RNA-seq expressions to predict tissues and cell types.

 

Genestack has an extensive collection of private and public genomics and transcriptomics datasets. This includes a collection of public microarray and RNA-seq experiments from public repositories, including well-annotated metadata. We have been processing them and accumulating an increasingly larger amount of gene expression data. We are now interested in leveraging this data to learn the expression signatures of specific phenotypes: for instance, training a predictive model that assigns tissue on the basis of a sample’s expression profile. This approach can then be extended to predict other phenotypical attributes such as disease, cell line, or cell type. We are also interested in applying machine learning approaches to RNA-seq datasets at the single-cell resolution, for cell subpopulations discovery.

 

In this project, you will help with researching appropriate machine learning strategies, designing and implementing them, as well as performing benchmarking/validation analysis.

 

Genomics-based Crop Analysis Using Multi-omics Gene-Trait Networks

 

Aims:  
(1) To integrate multi-omics data and literature mining for constructing plant knowledge networks.
(2) To develop query/visualisation features for interrogating the knowledge network and finding candidate genes associated with phenotypes.

 

Genestack is working on an Innovate UK funded collaborative project with Rothamsted Research, a major UK agri-genomics research centre and the longest running agricultural research station in the world. Over the past ten years, Rothamsted Research has been developing techniques to integrate multi-omics data and literature mining for gene networks analysis. This will enable scientists to use high-throughput bioinformatics technologies to accelerate genomics-based crop improvement and protection.

 

These tools have now been integrated into Genestack: users now have access to a simple, streamlined process from data collection, knowledge network building, to knowledge discovery. Furthermore, these tools have now been integrated with other Genestack applications to aid the knowledge discovery process. For example, using the homology inference application, users can now link networks between different species via protein homology relationships: allowing a novel organism to be analysed immediately using a well-annotated organism.

 

In this project, you will assist our effort with the construction and exploration of the knowledge network in several directions:

  • Identifying and integrating new -omics dataset to enhance the generated knowledge network
  • Automated summarisation: generating text summary of a list of genes/proteins based on the information in the network. This summary is useful to infer the function of a gene/protein
  • Improving upon the current network-based visual analytics, e.g. overlaying expression data on the network

 

You will work with researchers at Rothamsted, and selected industry participants who will help steer the project by contributing with specific requirements and use cases.

 

Requirements: 

–Scripting proficiency with Python and/or R, including data analysis libraries (e.g. Bioconductor, pandas, numpy, etc.)
–Proficiency with the UNIX command-line
–Good knowledge of statistics
–Being passionate about finding a rigorous way of investigating an ill-defined biological question
–Ability to report scientific findings

 

Ideally: 

–Experience in bioinformatics data analysis: knowledge of data types, pipelines, databases, and typical challenges/caveats associated with bioinformatics analysis

Senior Java DeveloperCambridge, UK or St.Petersburg, Russia

Genestack is a bioinformatics platform where biologists store, process and research genomic data.

 

We are looking for a programmer who can find elegant solutions and get things done in time.

 

We follow agile practices and work in small iterations. We expect our candidate to be able to split a large task into a number of smaller steps, each one having a measurable or visible result.

 

Tasks include, but are not limited to: development and support of the system core, API and platform applications, data storage optimization, and cluster process management (Amazon, etc).

 

Requirements:

–Strong Java skills
–Good knowledge of CS
–Knowledge of basic principles of OOP, data structures and algorithms
–Capability to work independently at all stages from planning to production code
–Knowledge of code optimization, profiling, and testing
–Ability to read other people’s code and refactoring experience
–Experience in web application development
–Intermediate level of English
–Interest in working with biological applications and data

 

Nice to haves:

–Experience with big data
–Knowledge of biology and genetics
–Python, R
–Experience with Unix-like systems
–Experience with JS/HTML/CSS

 

We offer:

–Competitive salary
–Flexible working hours
–All the things you find in a startup: interesting problems, stimulating environment, loads of independence 

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