Totus Medicines, an innovative therapeutic drug discovery biotech, is just emerging from stealth. With a $40M Series A, Totus is poised to revolutionize drug discovery. For the first time, Totus is enabling genome-scale drug discovery to allow the identification of precision medicines for hard-to-drug targets. At its core, Totus is a chemical biology company. Totus’ novel proprietary platform allows screening at a rate more than 100,000 times faster than competitive technologies. Totus has pioneered the development of selective medicines for patients with genetically defined cancers and plans for its first program to enter Phase I by end of 2022. Totus also plans to expand their platform to create therapeutics targeted at neurodegeneration, infectious diseases, and others. We are searching for talented, passionate, high-energy candidates to join our growing team.

We are seeking a candidate with skills to lead Data Engineering. Reporting to the Head and VP of Data Architecture and Engineering, you will be part of a functionally integrated team, bring expertise in advanced data engineering for large-scale life science data systems. We are ideally looking for someone to be part of a world-class scientific data platform to drive forward drug discovery to help save lives. You will be part of a dynamic organization and therefore you should be comfortable in a “building-mode” biotech setting where you will shape the company’s scientific strategy, culture, and mission. If this role excites you, reach out to Totus today.

What you bring:

  • Sc. or M.Sc. in data science with 5+ years of industry experience of data engineering and research IT in a life science setting
  • Demonstrated ability to reliably implement efficient and robust data pipelines, experience with managing an AWS environment, and a track record of experience with open source and enterprise database systems design
  • Expert in data modeling for relational, NoSQL, and graph models
  • Deep experience managing the scale, performance, functionality, and integrity of large-scale data platforms in a Cloud environment
  • Broad knowledge and experience with modern data platform compute and storage frameworks (e.g., Spark, Kafka, Parquet)
  • Strong software skills for preparing and extracting data using Python, R, and SQL
  • Knowledge of life science research data types (biological and chemical), ‘omics data and life science research platforms is important
  • Adaptability and eagerness to troubleshoot or adjust to scientific changes as necessary
  • Ability to communicate effectively with cross-functional teams and contribute to critical decision making
  • Big picture thinking with an enthusiasm to drive data science methods forward and work collaboratively to help develop novel therapies for patients

Responsibilities:

  • Establish a strategy to buildout and scale a scientific data pipeline to support data analytics, data modeling and knowledge capture
  • Provide technical leadership, strategy, and expertise in all aspects of data warehousing, Cloud computing and Big Data analysis pipelining
  • Create an environment of innovation and entrepreneurship that enables a scalable data architecture to support all scientific data across the organization
  • Provide hands-on system design, DB schema design, software engineering and overall systems architecture support for the organization
  • Interact and form close and interactive partnerships with key stakeholder groups, including chemistry, biology, computational chemistry, ML scientists and IT functions
  • Provide mentorship and development of the team and contribute to building a culture that embraces scientific excellence and integrity with a sense of urgency and collaboration

What We Offer

  • A knowledgeable, high-achieving, experienced and fun team

  • A diverse work atmosphere

  • The chance to be part of a growing startup and the next success story

  • The opportunity to shape our company culture

  • Constant learning and dynamic challenges to help you grow and be the best version of yourself

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