Computational Systems Biologist

As a Computational Systems Biologist at Tempus, you will design and execute complex computational research projects, analyze dynamic clinical and molecular datasets, and develop algorithms that address significant biological and clinical queries. Additionally, you'll engage in cross-functional collaboration, evaluate new healthcare technologies, and communicate your findings to both technical and non-technical audiences.

To qualify for the Computational Systems Biologist role at Tempus, candidates must hold a PhD in a quantitative field (like bioinformatics or statistical genetics) or possess equivalent experience. A demonstrated track record in machine learning applications with genomics data and proficiency in languages like R, Python, and SQL are also essential.

At Tempus, a Computational Systems Biologist will work primarily with large-scale clinical and molecular datasets, including transcriptome data. You'll be tasked with integrating and analyzing these datasets to derive meaningful insights that can lead to better treatment outcomes for patients.

Ideal candidates for the Computational Systems Biologist position at Tempus should be well-versed in machine learning algorithms, familiar with tools and languages such as R, Python, SQL, and experience with frameworks like tidyverse, TensorFlow, and data visualization libraries like ggplot and matplotlib. Knowledge of large transcriptome databases like TCGA and GTEx will also be beneficial.

Tempus is committed to fostering a culture of continuous learning and professional development. As a Computational Systems Biologist, you'll have opportunities to collaborate with diverse teams, engage in cutting-edge research, and access resources that allow you to teach others and also learn new techniques that enhance your skills and expertise.

When analyzing large datasets, I utilize a structured approach that involves data cleaning, exploratory analysis, and then employing specific algorithms based on the unique questions that arise from the data. Communication with team members to validate findings is also crucial, ensuring insights are actionable and clear.

During my PhD, I developed a machine learning model to predict patient outcomes based on genomic sequences. I implemented feature selection techniques to identify the most impactful variables and collaborated with clinicians to ensure the model's findings were meaningful for patient care.

When presenting complex results, I focus on simplifying the language, using visuals to represent data clearly and providing relatable examples that anchor my findings. Additionally, I encourage questions throughout my presentation to engage the audience and clarify any misunderstandings.

I keep myself updated on emerging healthcare technologies by subscribing to relevant journals, attending conferences, participating in webinars, and networking with industry professionals. This continuous learning approach helps me identify which new technologies could be integrated into my work.

In a previous project, we faced an unexpected dataset issue requiring immediate attention. I prioritized addressing this issue, reallocating my focus while communicating with my team about the change in direction and timeline. This helped ensure the project remained on track and met our tight deadlines.

I have significant experience integrating multi-modal data from various sources, such as clinical records, genomic sequences, and patient demographics. This involves harmonizing datasets to enable comprehensive analysis that provides deeper insights into cancer treatment and outcomes.

A Computational Systems Biologist plays a crucial role in precision medicine by applying data analysis techniques to uncover insights from complex biological data. By developing algorithms that inform targeted therapies, they help bridge the gap between research and clinical application, ultimately enhancing patient outcomes.

I start by performing thorough exploratory data analysis, checking for inconsistencies, missing values, and overall integrity of the datasets. I also use statistical methods to ascertain the reliability of the data and correlate findings with existing research to ensure accuracy.

I often use R and Python due to their versatility and extensive libraries for statistical analysis and machine learning. These tools allow for efficient manipulation of large datasets and provide powerful frameworks to apply complex algorithms effectively.

I believe effective communication and openness to different perspectives are key in interdisciplinary teams. I actively seek input from team members, sharing my insights while valuing their expertise—this approach fosters innovation and drives powerful outcomes.