Research Data Scientist-I

Why Sarepta? Why Now?
The promise of genetic medicine has arrived, and Sarepta is at the forefront. We hold leadership positions in Duchenne muscular dystrophy (DMD) and limb-girdle muscular dystrophies (LGMDs), and we currently have more than 40 programs in various stages of development in gene therapy, RNA and gene editing. In 2023, we launched our fourth therapy and the first ever gene therapy to treat Duchenne.
 

We’re looking for people who see unlimited potential in themselves and who are motivated by an unwavering commitment to patients.
 

What Sarepta Offers

At Sarepta, we care deeply about all the people in our community and believe in the importance of supporting them in all aspects of their lives. We aspire to maintain a culture that acknowledges people bring their whole selves to work, and we will strive to help everyone in our community integrate their work and personal lives while maintaining productivity. We are committed to offering a range of benefits and work-life resources designed to support people in the following areas:

• Physical and Emotional Wellness

• ​Financial Wellness

• Support for Caregivers
   

For a full list of our comprehensive benefits, see our website: https://www.sarepta.com/join-us
 

The Importance of the Role

As a Research Data Scientist- I, you will play an important role in advancing Sarepta's mission to develop transformative genetic therapies. By utilizing advanced computational biology and AI/ML models, this position plays a direct role in discovering and developing innovative treatments for Duchenne muscular dystrophy (DMD), limb-girdle muscular dystrophies (LGMDs), and other genetic disorders. The successful candidate will be instrumental in incorporating data-driven insights into the drug development process, ultimately improving patient care and outcomes.

The role of Research Data Scientist presents an exciting opportunity for individuals with a strong background in data science and machine learning who want to make a direct impact on patient care. In this hands-on position, the data scientist will research and develop advanced machine-learning models to tackle challenging biological questions related to drug discovery across various therapeutic modalities, including RNA therapies, gene replacement, and gene editing. As a data scientist, you will gain exposure to the entire drug development process and collaborate with both bench and computational scientists to create and implement computational solutions that support scientific projects. This is a fantastic opportunity to work at the forefront of precision genetic medicine, where your contributions could lead to the development of transformative therapies that may change patients’ lives.

The Opportunity to Make a Difference

• Develop and implement cutting-edge computational biology and AI/ML models to address challenging research questions that support various stages of drug discovery projects. 
• Develop advanced deep learning models to leverage internal and external experimental data to derive novel insights and support scientific decision-making. 
• Implement appropriate benchmarks to evaluate model performance, robustness, and scalability, and collaborate with scientists across the organization to validate model predictions. 
• Collaborate with research scientists across the R&D organization, to identify key scientific problems and curate relevant datasets for developing novel computational, machine learning solutions.
• Evaluate and adopt both proprietary and open-source tools to enhance computational biology and machine learning workflows. 
• Maintain and develop a well-documented codebase and documentation for ML/AI models and computational workflows. 
• Perform additional related tasks as assigned.

More about You

• PhD in Computational Biology, Machine Learning, Biomedical/Chemical Engineering, or a related field, along with 1-2 years of relevant experience, preferably in the biotech or pharmaceutical industry. A background in gene therapy and gene editing technologies is strongly preferred.
• Demonstrated expertise in conceiving and implementing machine learning and neural network models on biological datasets, with a solid understanding of feature selection, regularization techniques, model introspection, and interpretability.
• Proficiency in utilizing and adapting probabilistic learning or deep learning models, including Recurrent Neural Networks (R