Computational Biology, Data Scientist Internship - Summer 2024 | Cambridge, OH

of my knowledge. Job Description At Takeda, we strive to create an exceptional people experience for patients, communities, partners, and employees. As a patient-focused, values-based, R&D-driven global biomedical company, we know our decisions and actions affect people's lives.

We look to our values of Takeda-ism, which incorporates Integrity, Fairness, Honesty and Perseverance, and Patient-Trust-Reputation-Business as a guide in our decision-making process to ensure we do what's right - for our patients, our people and society. Are you ready to embark on a transformative journey with Takeda? Our paid 10-12-week summer internship program is designed to immerse you in a dynamic learning

environment, featuring a blend of developmental and social activities. As a Takeda intern, you'll have the opportunity to contribute to impactful projects that play a crucial role in shaping both our business and the well-being of patients.

This experience will not only allow you to apply your technical and business knowledge but also empower you to showcase your analytical strength and creative problem-solving skills. Seize this chance to gain hands-on experience, expand your skill set, and make a meaningful impact on the healthcare industry. We invite you to be a part of our innovative and collaborative team, where your contributions matter. For more information about Takeda, please

visit. Please note that Takeda does not provide intern housing or a housing stipend.

Project Outline: As a Summer Intern (hybrid position) in our Computational and Systems Biology (C&SB) team at Takeda (Cambridge, MA), your accountabilities will include actively contributing to the analysis of gastrointestinal (GI) disease multi-omics data, encompassing both single-cell and bulk expression datasets. You will apply advanced network and machine learning (ML) approaches to identify molecular signatures associated with IBD inflammation and fibrosis progression or response to treatment. Additionally, you will play a vital role in engaging in various forums, including meetings, scientific discussions, and presentations within our diverse research group.

Your participation in these activities will contribute to the collaborative and dynamic environment within the Gastroenterology Drug Discovery Unit (GI DDU) team, fostering interdisciplinary communication and knowledge exchange. This internship provides a hands-on opportunity to apply computational and systems biology techniques to real-world drug discovery challenges, enhancing your skills in data analysis, network biology, and machine learning, while actively contributing to ongoing research endeavors. How you will contribute: Conduct in-depth analysis of gastrointestinal (GI) disease multi-omics data, integrating insights from single-cell and bulk expression datasets.

Utilize computational tools and techniques to extract meaningful patterns and identify key molecular features relevant to GI diseases. Apply network biology approaches to uncover intricate relationships within GI disease pathways and molecular interactions. Implement machine learning (ML) methodologies to identify and characterize molecular signatures. Actively participate in meetings and scientific discussions within a diverse research group, contributing insights and sharing findings with team members.

Collaborate with cross-functional teams, including biologists, data science and engineering groups, translational medicine, and biomarker teams. Prepare and deliver presentations to communicate research findings, methodologies, and insights to both technical and non-technical audiences. Engage in knowledge exchange with team members and leverage the internship as an opportunity for professional growth and skill development. Foster interdisciplinary collaboration by actively engaging with key stakeholders, including biologists, data scientists, engineers, translational medicine experts, and biomarker teams.

Requirements / Qualifications: Currently enrolled in a relevant undergraduate or graduate program in computational biology or a related field. Strong analytical and data interpretation skills. Strong quantitative, analytical, problem solving and conceptual skills. Proficiency in programming languages commonly used in bioinformatics (e. g. Python, R). Familiarity with single-cell data analysis, biological network construction, and knowledge graph applications is a plus. Excellent communication and teamwork skills.

Proven record of accomplishment of teamwork, adaptability, innovation, initiative, and integrity. Currently authorized to work in the US (United States) without sponsorship. Takeda does not provide sponsorship for internship positions. Available to work full-time (40 hours/week), within the core business hours of 8:00am - 5:00pm, for 10-12 weeks during the summer months. Graduate, Ph D, MD student with completion of a minimum of one year of university studies. Must be enrolled in school the semester following your internship with Takeda. Please note that Takeda does not provide intern housing or a housing stipend.

The hourly pay rate range is $20.00 per hour - $46.00 per hour. The final pay rate offered for this internship may consider several factors including, but not limited to, location, skills, education, and experience. EEO Statement Takeda is proud in its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, interaction, interactionual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, status as a Vietnam era veteran, special disabled veteran, or other protected veteran in accordance with applicable federal, state and local laws, and any other characteristic protected by law.

Locations USA - MA - Cambridge - Binney St Worker Type Employee Worker Sub-Type Paid Intern (Fixed Term) (Trainee) Time Type Full time