Pivotal Life Sciences – AI Data Scientist – Job Description

The ideal candidate for the AI Data Scientist role at Pivotal Life Sciences will possess a Master’s or PhD in a relevant field, along with 3-5 years of hands-on experience in machine learning, deep learning, and data analysis, ideally in the life sciences sector. They should demonstrate proficiency in Python and experience with AI frameworks like TensorFlow and Keras, as well as familiarity with large language models and cloud computing platforms. Strong analytical and problem-solving skills, combined with effective communication and collaboration abilities, will enable them to thrive in a dynamic team environment focused on transforming investment processes in the life sciences industry.

Intro

If WHO you work for & with and WHY your company exists is at the top of your list of criteria for choosing your next opportunity, we encourage you to take a look at the roles we are hiring for at Pivotal Life Sciences (PLS) and join our growing team of data scientists/engineers working together toward a common goal—the health and care of the patient. We’re looking for people to not only join us—but to be a big part of the solution. To do more and be more. To do well and do good. If you have the ability to see the bigger picture and bring it to life, let's connect!

Locations: San Francisco, CA, USA

The Vision

PLS envisions becoming the life sciences investment industry's best tech-enabled investment platform. The AI (Artificial Intelligence) team aims to provide best-in-class intelligence support across all steps of the investment process, from deal sourcing to exit. The system will function as an additional team member and help augment the investment team to make better investments and build better companies for unmet therapeutic needs. Ultimately, PLS will become a scientifically driven investor across the life science ecosystem from academic spinouts to venture rounds and to exit. Come be part of our vision!

The Role

As a PLS AI Data Scientist, you will be a member of our new global Data and Artificial Intelligence team. This team’s goal is to build state-of-the-art data and AI technologies and products with strong research fundamentals for our life sciences investment arm. You will utilize your expertise in the data sciences and engineering to investigate and build data sources, models, and AI products that support the investment process. These models and AI products include disease mapping, portfolio management predictions, financial planning, and operational streamlining and optimizations. This is a great opportunity to work on a range of AI powered products in a growing team with exposure to the best life science companies today.

Key Responsibilities

AI Model Research & Development

Conduct research and development of AI models and algorithms, staying updated with the latest advancements in AI, ML, and NLP.

Evaluate, implement, and innovate state-of-the-art generative models (e.g., Large Language Models (LLMs)) and traditional ML/NLP models for life science investment use cases.

Focus on tailoring generative models to specific use cases, ensuring their performance and relevance to our applications.

Platform Development & Collaboration

Collaborate with researchers, data scientists, software engineers, and investors to create and improve a cutting-edge AI and analytics platform tailored to support investment decisions.

Act as an advocate for AI-supported investment processes across cross-functional teams, driving adoption and innovation.

Data Engineering & Integration

Perform data mapping, fusion, and feature engineering on complex biological, medical, financial, corporate, and other datasets to support AI/ML model inputs.

Work with diverse data sources, including PubMed, patents, SEC filings, biological databases, financial datasets, and others, ensuring their effective use in modeling pipelines.

Model Benchmarking & Improvement

Benchmark, evaluate, and document the performance of AI models, providing actionable recommendations for continuous improvement.

Requirements

Educational Background

Master’s or PhD in Computer Science, Artificial Intelligence, Applied Mathematics, Statistics, Machine Learning, or a related discipline.

Technical Expertise

3-5 years of experience in machine learning, deep learning, statistical data analysis, and complex data visualization (experience in the life sciences industry is a plus).

Proficient in Python, with hands-on experience in ML/AI frameworks such as TensorFlow, Keras, and Scikit-learn.

Familiarity with large language models (e.g., GPT-4, Stable Diffusion) and the ability to evaluate and adapt these models for specific use cases.

Strong knowledge of relational databases and SQL, including expertise in database architecture and data management.

Experience with cloud computing platforms, preferably AWS (Amazon Web Services), and AI platforms like SageMaker or MLFlow.

The ideal candidate would have some experience with text summarization and information extraction, Retrieval-Augmented Generation (RAG), agentic systems, time-series forecasting, supervised learning (logistic regression, tree-based models), and deep learning.

Development & Workflow

Ability to design and implement AI solutions within a structured Software Development Life Cycle (SDLC).

Familiarity with software development best practices, including unit testing, code reviews, and version control systems.

Soft Skills

Exceptional analytical, problem-solving, and presentation skills.

Strong verbal and written communication skills, with the ability to work independently and collaboratively.

Proficiency in English.

Other Requirements

Must have a valid U.S. work visa and will not require employer sponsorship now or in the future.

Salary Range $142k-$200k + up to 20% Annual Performance Bonus

Hybrid work schedule: Minimum 3 days per week in the San Francisco office with the option to work remotely 2 day per week.

Generous benefits package: Fully paid healthcare, monthly reimbursements for gym, commuting, cell phone & home Wi-Fi, free lunch.

Pivotal

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