Data Scientist

Job Description:

At Parallel Bio, we are using the immune system to unlock safer, more effective drugs. By creating access to genuine human biology in a diverse, reproducible, and scalable way we’re changing the way researchers find, test, refine, and deliver these life-changing therapies.

The human immune system is the master regulator of health and disease in our bodies. Current tools used to discover drugs that target the immune system are wildly inadequate. To fix this critical gap, Parallel Bio created a human ‘immune system in a dish’ to rapidly accelerate the development of new drugs and immunotherapies more likely to work in patients. This means developing drugs that are known to safely work in humans before the first patient receives the first dose and an end to needlessly destroying millions of lab animals in the pursuit of human health.

By blending this biological innovation with the latest in high-throughput lab automation and integrated data capture & analysis, we’re creating a new type of biotech – one that brings the advantages of cloud computing to next-generation wet lab studies.

The Role

Note to candidates: The requirements listed here are an exhaustive list of useful traits but not a hard list of requirements. Candidates who don't fulfill one or more of the listed requirements but still think they are strong candidates are encouraged to apply.

As a Data Scientist at Parallel Bio, you will play a crucial role embedded within our tech team, primarily serving our science team. You'll accelerate our understanding of complex biological datasets, develop robust analytical pipelines, and effectively communicate insights to drive our drug discovery efforts. This is a highly collaborative role where your data expertise will directly impact our scientific advancements.

The immediate high-level goals for the person in this role are the following:

• Lead data visualization efforts across every single assay, providing clear and insightful presentations of complex biological findings.

• Support flow cytometry data analysis and visualization specifically, working closely with our scientists.

• Drive data visualization for transcriptomic data and other assay leads.

• Act as a critical analytical resource for the science team, taking on analysis tasks to help them quickly understand what types of analyses to run and interpret results.

• Collaborate closely with software engineers to quickly iterate and build user-friendly tools that enhance our data analysis capabilities.

While this role primarily focuses on data analysis and visualization, you will work closely with scientists, automation engineers, and software engineers to advance our research efforts. You will be responsible for conducting routine analyses, troubleshooting data issues, and generating high-quality insights that drive key discoveries.

Areas of Responsibility

Data Analysis & Visualization

• Take ownership of data analysis and visualization for various assays, including flow cytometry, transcriptomics (RNA-seq), and OMEX+ data.

• Ensure high-quality, insightful presentation of complex biological findings for internal and external stakeholders.

• Contribute to understanding and visualizing large, complex biological datasets, especially from omics technologies.

Scientific & Technical Collaboration

• Support the science team by conducting analyses, identifying appropriate analytical approaches, and helping to quickly derive insights from experimental data.

• Collaborate with software engineers to quickly iterate and build tools that enhance data analysis capabilities for our users.

• Effectively present scientific findings and data visualizations to both internal teams and external stakeholders.

• Act as a key bridge between biological insights and potential future Machine Learning (ML) initiatives, laying groundwork for future advanced analytical capabilities.

Pipeline Development & Data Rigor

• Identify, develop, and ensure the rigorous application of data pipelines, performing statistical analysis to validate scientific questions and outputs.

• Contribute to the strategic thinking around data modeling and overall data handling strategies for large datasets.

• Provide expertise in handling and analyzing complex biological data, particularly RNA-seq, and assist scientists in establishing and validating data pipelines from wet lab experiments.

Job Requirements

Education:

• Degree: A PhD or Masters in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science, Engineering)

Technical Abilities

• Programming: Fluency in Python for data science and demonstrated strong programming ability.

• Data Analysis Experience and Tooling: Strong data analysis skills and familiarity with statistical software and api-based data sources

• Modeling: Understanding of fundamental modeling techniques (e.g., regression, classification, clustering, time series analysis, deep learning concepts).

• Cloud & Software: Familiarity with cloud computing services (e.g., AWS) and knowledge of software development principles is a plus.

Biological Domain Expertise

• A strong background in biology, specifically immunology, gained through formal education or extensive practical experience.

• Some knowledge of bioinformatics is required.

• Experience working on challenging biological problems and with biological datasets is a plus.

• Interest in immunology and modeling complex diseases.

• Experience with drug discovery platforms is a plus.

Organizational Abilities

• Strong attention to detail and ability to follow experimental protocols precisely.

• Ability to multitask and manage time efficiently in a fast-paced research environment.

• Strong problem-solving skills and willingness to iterate on experimental challenges.

• High personal standards and an excellent track record of driving projects to completion.

• Comfort and demonstrated ability to thrive in a fast-moving, high-growth startup environment with shifting priorities.

Communication & Collaboration

• Strong communication skills. We are building a cross-functional team, so excellent communication and a deep desire to learn from others is key.

• Ability to clearly explain complex data insights to both technical and non-technical stakeholders.

• Curiosity and a natural inclination to ask questions and follow data to generate hypotheses.

• A quick learner with a strong "wheels turning" mindset.