Additional EMBL resources
Interview-based career profiles for this career area
Michael Kachala, Global Head of Data Science at Bayer Consumer Health
Career area: Data science, analytics & software
Commercial data analytics and software roles are a common career destination for computational biologists, bioinformaticians and scientists from other quantitative disciplines (e.g. maths and physics) with significant programming experience. Some roles are also a potential career option for life scientists with a wet-lab biology background and experience in programming data analysis pipelines.
Employers include specialist software/high-tech companies, ranging from small start-ups to large multinational companies – as well as companies with a broader portfolio, who develop in-house digital solutions to support their products and services.
Note: there are also computational biology roles within life science R&D, including data science roles that use biological/research data to deliver insights relevant to drug development pipelines: for these roles, please also see our industry R&D career area article.
Roles and responsibilities
There are different types of roles in this area:
- Software developer / engineering roles focussed on the design and programming of specific software, apps or other digital tools. This may include maintenance & building new features, or building new tools from scratch.
- Data science roles that aim to generate meaningful insights from large datasets by creating specific, reproducible data analysis or predictive analytics pipelines using R, Python and other tools – often (but not always) applying artificial intelligence.
- More technical roles (e.g. DevOps, database systems); these are only possible for those with a strong computational science background
The exact tasks will vary by role / company but often involve:
- Coding.
- For software solutions, often collaboratively, with a focus on efficient, maintainable and stable solutions.
- For data science: pull data from internal databases; assess, clean, transform & visualize / summarize data; potentially build reporting dashboards, machine learning models, or an application programming interface (API).
- Meeting with end-users or ‘problem owners’ to understand:
- for software: the needs and requirements of the end-users.
- for data science: the data being looked at, and what analysis might bring value and insight.
- Strategic planning of the to-be-delivered solutions, and their (continuous) deployment.
- Attending project meetings where progress and upcoming tasks are discussed, and work distributed including (collaboratively) deciding on task prioritization & assignments.
- note: many software companies use a method called ‘SCRUM’ for project management that has a focus on allowing ‘agile’ development
- Communicating with team members working in product management, IT management, software architecture, user experience and DevOps,
- Training / staying up to date with new developments
- Presenting progress / insights to managers and ‘problem owners’.
- Involvement in other business aspects like overall strategy, patent preparation, managing /mentoring teams etc.
Knowledge and skills
Data science roles require experience accessing and analysing large datasets, and developing/programming algorithms to process the data using relevant programming languages and tools (e.g. R/Python [and relevant toolkits], version control etc.). A good knowledge of statistics and experience with machine learning methods is advantageous. Building a portfolio of small projects is often recommended as a way of furthering and demonstrating your knowledge. Online courses can be helpful to build relevant skills and a range of companies offer intensive data science courses (bootcamps) with capstone projects or short internships for those with a lack of experience.
Software development roles require hands-on experience with developing tools in at least one programming language relevant to the specific role. These roles require knowledge of software development best practices and theory in order to build stable software that integrates with other tools from the company and can be maintained long-term.
Broader skills are also critical for both types of role. Strong listening & critical thinking skills, willingness to ask questions, and an ability to present back the results clearly are required. Data scientists are expected to understand the problems that the business/client are trying to investigate, and to deliver meaningful analysis that gives actionable insights. Similarly, software teams must develop solutions that clients want to buy – the solution must solve a need the client has and be user-friendly. Ability to work in teams is also a must as the software / data analysis solutions are generally developed by a group of people. Given the range of projects and fast pace of technology developments, a love of learning is also often mentioned as an asset for data science roles.
In our careers and skills survey, 11 data science or software professionals told us the competencies they use most in their daily work The most frequently selected competencies were:
- team work (selected by 73% of respondents)
- broad scientific knowledge (selected by 64% of respondents)
- resilient problem solving (selected by 45% of respondents)
- visualizing data and ideas (selected by 45% of respondents)
- delivering presentations (selected by 45% of respondents)
Language skills
While some companies will work in the local language, many software companies have English as the main language. Obtaining a position without fluency in the local language is possible in many companies.
Career entry and progression
Data science roles are directly accessible for PhDs and postdocs with experience in data analysis pipelines written in R or python, and ideally some knowledge of SQL (which is often used to extract relevant data from databases). For some roles, experience with machine learning techniques may also be advantageous or required. For those with less experience, the career path is accessible with additional training.
Software development roles are often directly accessible for those with a formal computational degree, or those with computational projects who have significant programming experience.
Interviews may include technical questions focused on theory (e.g. on data structure and specific algorithms), as well as coding tests that apply this, so it is advisable to refresh your knowledge in relevant computational science topics in advance of interviews.
Note: many of our former PhDs and postdocs have entered roles focussed on developing/maintaining scientific software, but some former fellows also work on business software & digital tools at other companies including in the high-tech sector (Google, Amazon and IBM), financial services etc.
Career progression:
Common career development paths in this career area include:
- gaining in seniority and becoming a technical expert in specific areas (e.g. software architecture, data science expert, tech lead),
- moving into a more people-management role supervising a team
- moving sideways into other types of roles that have a more coordination/strategy development and less technical focus (e.g. product management)
- starting a consultancy or free-lance business
Example job titles
- (Software) developer / engineer
- Computational scientist
- Data scientist
- Machine learning specialist, machine learning engineer
- Data analyst
- Business intelligence analyst
- Systems engineer
- HPC engineer
- Programmer
- Database programmer
- Full-stack developer
- Software architect, tech lead (more senior roles)
Why consider this career area?
In our careers and skills survey, 10 data science / software professionals told us what they appreciate most about their work. The most common selections were, that the work:
- Is intellectually stimulating
- Provides opportunities for personal growth
- Provides stable employment
Want to learn more?
Sources / further reading
- Written articles (first-person experience)
- Data science career profiles on this blog (see above)
- My Life as a Life Sciences Software Developer – BitesizedBio
- Overview articles:
- Careers Book – BUILD A CAREER IN DATA SCIENCE, Emily Robinson & Jacqueline Nolis, ISBN 978-1617296246; available in the EMBL Library ***highly recommended***
- Resources:
- Kaggle – a community of data scientists and machine learners
- Data Scientist & Analyst LinkedIn Group or The Data Networking Group.
- Podcast: Not so standard deviations Podcast
- Towards data science – website publishing articles on data science topics
- UCSF data science career exploration pages
- InterSECT Job Sim simulation library – including:
Further internal resources
For EMBL fellows
Within EMBL, further internal resources (e.g. recorded career seminars) can be found on our career exploration intranet pages.
Informational interviews
For all career areas, we highly recommend first learning more about the careers using the resources above, then conducting informational interviews to gain further insights directly from former PhDs working in career areas that interest you.
Last update: November 2022
