Senior Manager / Director, Integrative Phenotyping

Key Skills

AWS, Data Modeling, Keras software library, Machine learning techniques, PyTorch, TensorFlow

Job Description

The Opportunity

insitro's mission is to bring better drugs faster to the patients who can benefit most, through machine learning and data at scale. To address that goal, our discovery strategy integrates insights from multiple phenotypic readouts, spanning diverse high-content data modalities; we utilize data from public and proprietary human cohorts, and from in vitro cellular systems, generated by our proprietary, automated wet-lab platforms.

In this role, you will be responsible for leading and growing a team of outstanding machine learning scientists in developing cutting edge machine learning/artificial intelligence (ML/AI) methods that extract meaningful, disease-relevant insights leveraging these diverse data modalities to unlock new therapeutic hypotheses. Aligning with variation in human genetics, you will identify causal nodes that meaningfully modulate disease-relevant phenotypic traits, derisking our hypotheses and increasing our probability of success in the clinic, allowing us to deliver on our mission.

You will be joining an exciting techbio startup that has long-term stability due to significant funding, but that is still very much in formation. You will have ample opportunities for growth and impact. You will work closely with a diverse and talented team, learn a broad range of skills, and help shape insitro's culture, strategic direction, and outcomes. Join us, and help make a difference to patients!

Responsibilities

• Lead and grow a team of outstanding machine learning (ML) scientists
• Guide your team to develop and deploy ML methods for interpreting complex phenotypes, and for integrating multi-modal analyses across diverse phenotypic readouts
• Collaborate with other leaders in the company to achieve key goals:
• With our therapeutic area and clinical development colleagues to develop new biomarkers for patient selection and clinical endpoints
• With our statistical genetics team to associate human genetics with clinical outcomes
• With our corporate development team to identify and acquire new, high-value data sets to drive our analysis
• With our lab scientists and with other ML teams (Imaging, Omics) to design experiments that generate datasets that are fit for purpose for machine learning, including ones generated explicitly for training ML models
• Lead yearly and quarterly planning, set impactful goals, and align with cross-functional stakeholders
• Engineer robust, reusable platform components in partnership with the software engineering team

About You

• Expertise in developing and deploying state-of-the art ML methods for interpreting and extracting signals and clinically-relevant insights from complex data modalities, including hands-on experience with modern multimodal ML techniques
• Demonstrated ability to lead a team of ML scientists and engineers to plan, execute and deliver a full ML solution for challenging, real-world problems: sourcing and qualifying training data; designing and implementing ML models; testing & benchmarking
• Extensive experience with diverse clinical data modalities, including some subset of:
• Physiological recordings (e.g., EEG, ECG, etc.)
• Molecular readouts, including omics from blood or tissue
• Imaging modalities (e.g., histopathology, MRI, DEXA, CT, etc.)
• Clinical records, including individual demographics, drugs administered, clinical outcomes, etc.
• Experience with human genetics is a nice to have
• Understanding of cellular data modalities, such as fluorescent microscopy or RNAseq, or desire to grow in this area
• Conversational understanding of human physiology or disease biology (e.g., neuroscience, cancer biology).
• Publication record of meaningful, high-quality contributions in relevant ML, ML for health, computational biology, or biomedical venues
• Demonstrated track record of producing high-quality, reusable ML code, including experience developing models using modern deep learning frameworks (such as PyTorch, TensorFlow, or Keras) and with cloud computing (preferably AWS)
• Proven ability to mentor, coach, and lead junior scientists
• Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions, including a track record of working collaboratively with clinical or experimental life scientists
• Passion for providing better medicine to patients in need

Compensation & Benefits at insitro

Our target starting salary for successful US-based applicants for this role is $240,000 - $305,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.

This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.

In addition, insitro also provides our employees:

• 401(k) plan with employer matching for contributions
• Excellent medical, dental, and vision coverage (insitro pays 100% of premiums for employees on our base plans), as well as mental health and well-being support
• Open, flexible vacation policy
• Paid parental leave
• Quarterly budget for books and online courses for self-development
• Support to occasionally attend professional conferences that are meaningful to your career growth and development
• New hire stipend for home office setup
• Monthly cell phone & internet stipend
• Access to free onsite baristas and cafe with daily lunch and breakfast
• Access to free onsite fitness center
• Commuter benefits

About insitro

insitro is a drug discovery and development company using machine learning (ML) and data at scale to decode biology for transformative medicines. At the core of insitro’s approach is the convergence of in-house generated multi-modal cellular data and high-content phenotypic human cohort data. We rely on these data to develop ML-driven, predictive disease models that uncover underlying biologic state and elucidate critical drivers of disease. These powerful models rely on extensive biological and computational infrastructure and allow insitro to advance novel targets and patient biomarkers, design therapeutics and inform clinical strategy. insitro is advancing a wholly owned and partnered pipeline of insights and therapeutics in neuroscience, oncology and metabolism. Since launching in 2018, insitro has raised over $700 million from top tech, biotech and crossover investors, and from collaborations with pharmaceutical partners. For more information on insitro, please visit www.insitro.com.