Job Introduction: Supporting Corporate Banking Relationship Managers on a portfolio of corporate accounts. Providing support to the Relationship Management team on client management and business development activities, client servicing, administration & operational support, coordination with internal & external stakeholders.

Principal Responsibilities: Liaise with and help coordinate activities of the client service team (across products) in order to ensure a coordinated approach to enhancing the overall client relationship. Act as a point of contact for clients and internal HSBC colleagues, and effectively manage day-to-day requirements of the portfolio, along with the Relationship Manager. Establish strong relationships with key individuals in clients’ management teams. Work closely with Business Credit Officers (BCOs) and ensure credit applications (including both renewals and new money applications) and prepared and submitted on time. Handle any credit related queries along with the BCOs. Prepare internal approvals required for maintaining ongoing client relationships and specific transactions (including Customer Due Diligence, Pricing approvals, Credit, senior management and other internal approvals). Assist the Relationship Manager in negotiation of transaction-specific documentation. Assist in the development and maintain individual client plans in conjunction with the Relationship Manager. Understand and ensure compliance with all relevant internal and external rules, regulations and procedures that apply to the conduct of the business in which the jobholder is involved. Responsible for AML, Customer Due Diligence and other regulatory related responsibilities associated with Relationship Management.

Requirements: Minimum Graduation or as required for the role, whichever is higher. Mandatory to successfully complete Anti-Money Laundering and Sanctions training and post-course assessment, as required.

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Our full-stack antibody drug development platform uses machine learning to drive every stage from discovery to optimization. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom LIMS++ data management and orchestration layer to automatically update and deploy the latest models.

As a successful candidate, you will apply your world-class machine learning skillset to refine and expand this state-of-the-art protein engineering platform. Success will mean not only hands-on methods development, but helping shape the direction for future machine learning research, and actively participating in the application of our platform to the accelerated design of new therapeutics.

Key Responsibilities

• Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
• Provide leadership, technical guidance, and mentorship to other machine learning and data science FTEs and interns.
• Help set strategy for future machine learning research, driven by a strong high-level understanding of BigHat programs and operations as well as real-world drug development challenges.
• Develop, refine, and deploy de novo design methods for generating initial hits to challenging, therapeutically interesting targets.
• Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties.
• Maintain an in-depth understanding of the current state-of-the-art in machine learning-driven protein engineering, both in the literature and at BigHat.
• Share your findings at top-tier conferences and publish in leading scientific journals to advance the field of protein engineering.
• Provide machine learning expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
• Collaborate with our engineering team to ensure maximal efficiency in the automated and agentic deployment of our latest models to our therapeutics programs.
• Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. to identify inefficiencies or potential improvements in BigHat’s platform, and plan and prioritize machine learning methods development accordingly.

Skills, Knowledge & Expertise

• PhD in machine learning/computer science or in the hard sciences with 5+ years experience post-graduation in developing and applying novel machine learning methods, and a strong quantitative background.
• Publications in major machine learning conferences and/or leading journals, and an extensive demonstrable track record developing and applying novel machine learning in industry.
• Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices.
• Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
• Enjoys a fast-paced environment and excels at executing across multiple projects.
• Familiarity with the current state-of-the-art in machine learning-driven protein engineering
• Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, familiarity with antibody biology and drug development, and experience training and deploying models on AWS.

Benefits

The salary estimated for this position is $254,000 - $290,000 + bonus + options + benefits. Compensation will vary depending on job-related knowledge, skills, and experience. Actual compensation will be confirmed in writing at the time of the offer.

What BigHat Offers:

• Range of health insurance plan options through Anthem and Kaiser (monthly credit if benefit waived)
• Dental, and vision coverage through Guardian
• Additional well-being benefits through Nayya, OneMedical, Wagmo, Rula, and more
• 401(k) with company match
• DTO, two weeks of company-wide shutdown, and 12 company holidays
• Paid parental leave

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