Band

Level 3

Job Description Summary

We are seeking a highly skilled and motivated scientist to join our laboratory, specializing in NGS and in advanced spatial and single-cell genomics. The successful candidate will be responsible for the planning, execution, and optimization of experiments using spatial transcriptomics and single-cell protocols and instrumentation. This role is pivotal for supporting research projects, driving innovation, and ensuring high-quality data generation.

 

Job Description

Internal Job Title: Senior Scientist I

Position Location: Cambridge, MA, onsite

* Novartis is unable to offer relocation support for this role: please only apply if this location is accessible for you. #LI-Onsite

Key Responsibilities:

• Experimental Design and Execution:

• Plan and perform experiments utilizing state-of-the art single cell and spatial profiling platforms.

• Implement and optimize protocols for sample preparation, library construction, and sequencing.

• Ensure rigorous documentation and reproducibility of all laboratory procedures.

• Instrument Operation and Maintenance:

• Operate and maintain relevant instruments, including troubleshooting and routine care.

• Train and mentor junior staff on instrument use and protocol adherence.

• Data Analysis and Interpretation:

• Perform initial data processing for spatial and single-cell genomics datasets using appropriate bioinformatics tools.

• Collaborate with Data Scientists to interpret results, prepare reports, and present findings internally.

• Collaboration and Communication:

• Work closely with cross-functional teams, including molecular biologists, bioinformaticians, and project leaders at Novartis Biomedical Research, local and remote.

• Contribute to experimental planning and reporting.

• Quality Control and Compliance:

• Document work and monitor quality control measures to ensure data integrity and compliance with laboratory standards.

• Stay current with advances in single-cell and spatial genomics technologies and integrate best practices into lab workflows.

Essential Requirements:

• BS or MS in Molecular Biology, Genomics, Biochemistry, or a related field, with 4+ years of hands-on experience with spatial transcriptomics and/or single-cell sequencing technologies. This is not a PhD level position.

• Strong understanding of molecular biology techniques like PCR, qRT-PCR, enzymatic reactions, DNA & RNA purification and manipulation.

• Proficiency in protocol development, troubleshooting, and optimization for complex genomics assays.

• Knowledge of NGS library preparation and sequencing workflows.

Highly Desirable Requirements:

• Experience with tissue processing and imaging, IHC, microscopy.

Application Instructions:

Interested candidates should submit a CV and cover letter that includes a summary of relevant experience with single-cell and spatial omics protocols and instrumentation.

The salary for this position is expected to range between $93,800 and $174,200 per year. The final salary offered is determined based on factors like, but not limited to, relevant skills andexperience, and upon joining Novartis will be reviewed periodically. Novartis may change the publishedsalary range based on company and market factors.

Your compensation will include a performance-based cash incentive and, depending on the level of therole, eligibility to be considered for annual equity awards.

US-based eligible employees will receive a comprehensive benefits package that includes health, life anddisability benefits, a 401(k) with company contribution and match, and a variety of other benefits. Inaddition, employees are eligible for a generous time off package including vacation, personal days,holidays and other leaves.

To learn more about the culture, rewards and benefits we offer our people click here.

 

Salary Range

$93,800.00 - $174,200.00

 

Skills Desired

Analytical Thinking, Chemical Biology, Chemistry, Clinical Research, Clinical Trials, Collaboration, Curious Mindset, Data Analysis, Detail-Oriented, Ethics, Gene Therapy, Immunology, Machine Learning (ML), Mammalian Cell Culture, Medical Research, Molecular Biology, Patient Care, Science