
Senior Life Sciences Analyst, AI Model Training
Norstella · United States
- Hybrid
- Full-time
- $120,000 / year
- United States
Job highlights
- Analyze AI model outputs for life sciences.
- Calibrate models using human judgment and expertise.
- Develop evaluation rubrics and quality criteria.
- Collaborate with data scientists and engineers.
- Identify and address model failure modes.
About the role
Senior Life Sciences Analyst, AI Model Training
About Us
Why Norstella? Norstella unites market-leading companies that all have a shared goal of improving patient access. Each organization (Evaluate, Citeline, MMIT, Panalgo, The Dedham Group) delivers must-have answers for critical strategic and commercial decision-making.
Together, We Help Our Clients
- Assess the market need and competitive landscape
- Know precisely which drugs to prioritize in their portfolios
- Find out where the launch difficulties will be—before they’re difficulties
- Track and improve market access post-launch
By combining the efforts of each organization under Norstella, we can offer an even wider breadth of expertise, cutting-edge data solutions and expert advisory services alongside advanced technologies such as real-world data, machine learning-driven predictive analytics. At Norstella, we don’t just deliver information and insights. We deliver answers you can act on.
Job Description
About the role:
As a Senior Life Sciences Analyst for Model Training at Norstella, you will sit at the intersection of deep clinical and scientific domain expertise and applied AI development. This role will be embedded within a group of life science thought leaders, but will interface across cross-functional teams of data scientists, machine learning engineers and data engineers. Your work centers on the preference and judgment layer of model development — through scoring and ranking outputs, you will calibrate models on dimensions of quality, accuracy and clinical reasoning such that they are able to handshake with persona use cases. Your judgment becomes the signal that bridges a capable model and a genuinely useful one, and will play a critical role in our efforts to deliver predictive analytics and insights across clients.
Responsibilities
- Evaluate model outputs across clinical, regulatory, and life sciences tasks by rating individual responses, ranking pairs and lists, and rewriting outputs to demonstrate the preferred response.
- Define and maintain evaluation rubrics, preference dimensions, and quality criteria (factual accuracy, clinical reasoning quality, etc) that govern model outputs and preference alignment.
- Act as an expert-in-the-loop for how life science models are commercialized with clients, including ongoing support for implementation.
- Advise and assist with interpretation of model behavior, bridging the desired behavior back to the dataflow inflection point, working with the relevant teams to adjust as needed.
- Partner with data scientists and MLEs to walk feedback into data collection pipelines.
- Conduct red-team and adversarial evaluation of models, surfacing subtle failure modes — hallucinations, citation errors, unsupported clinical claims, regulatory missteps — that automated metrics cannot reliably catch.
- Analyze preference data and model behavior to identify systematic gaps, then partner with the fine-tuning SMEs and the data science team to decide how each gap is addressed through preference data.
- Conduct new proofs of concept for novel domain capabilities.
- Contribute to Norstella’s knowledge graph and taxonomy work and help design new agentic workflows based on domain-grounded language models.
The Guiding Principles For Success At Norstella
01: Bold, Passionate, Mission-First
We have a lofty mission to Smooth Access to Life Saving Therapies and we will get there by being bold and passionate about the mission and our clients. Our clients and the mission in what we are trying to accomplish must be in the forefront of our minds in everything we do.
02: Integrity, Truth, Reality
We make promises that we can keep, and goals that push us to new heights. Our integrity offers us the opportunity to learn and improve by being honest about what works and what doesn’t. By being true to the data and producing realistic metrics, we are able to create plans and resources to achieve our goals.
03: Kindness, Empathy, Grace
We will empathize with everyone's situation, provide positive and constructive feedback with kindness, and accept opportunities for improvement with grace and gratitude. We use this principle across the organization to collaborate and build lines of open communication.
04: Resilience, Mettle, Perseverance
We will persevere – even in difficult and challenging situations. Our ability to recover from missteps and failures in a positive way will help us to be successful in our mission.
05: Humility, Gratitude, Learning
We will be true learners by showing humility and gratitude in our work. We recognize that the smartest person in the room is the one who is always listening, learning, and willing to shift their thinking.
Qualifications: The skills you bring to the table:
- Graduate degree in life sciences, medical sciences, computer science or equivalent professional experience.
- At least 3 years of professional experience in production-grade life science datasets, including with AI-enabled applications.
- Experience working with structured publishing platforms and data tools; comfort with automation concepts.
- Experience working with and statistically analyzing large and complex data sets, including data cleaning and preprocessing.
- Excellent problem-solving skills and the ability to work independently.
- Excellent communication skills, especially between technical and non-technical teams.
Bonus Points If You Have Experience In
- Experience working with Generative AI, especially LLMs, including agents, throughout the entire software development lifecycle (SDLC).
- Experience creating MCPs and consuming them into Agentic workflows.
- Experience in fast-paced, novel product development.
Travel: None required
Benefits
- Medical and prescription drug benefits
- Health savings accounts or flexible spending accounts
- Dental plans and vision benefits
- Basic life and AD&D Benefits
- 401k retirement plan
- Short- and Long-Term Disability
- Education benefits
- Paid parental leave
- Paid time off
Norstella is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, pregnancy or maternity, gender reassignment, race, color, nationality, ethnic or national origin, religion or belief, disability or age. Our ethos is to respect and value people’s differences, to help everyone achieve more at work as well as in their personal lives so that they feel proud of the part they play in our success. We believe that all decisions about people at work should be based on the individual’s abilities, skills, performance and behavior and our business requirements. Norstella operates a zero-tolerance policy to any form of discrimination, abuse or harassment.
All legitimate roles with Norstella will be posted on Norstella’s job board which is located at norstella.com/careers. If a role is not posted on this job board, a candidate should assume the role is not a legitimate role with Norstella. Norstella is not responsible for an application that may be submitted by or through a third-party and candidates should proceed with extreme caution if a third-party approaches them about an open role with Norstella. Norstella will never ask for anything of value or any type of payment during or as part of any recruitment, interview, or pre-hire onboarding process. If you are aware of or have reason to believe a job posting purportedly for a role with Norstella is fraudulent or otherwise not authorized by Norstella, please contact the Company using the following email address: ApplicationHelp@norstella.com
Key skills/competency
- Senior Life Sciences Analyst AI Model Training
- Clinical Domain Expertise
- AI Development
- Data Science
- Machine Learning Engineering
- Data Engineering
- Model Evaluation
- Preference Alignment
- Life Sciences Data
- Generative AI
Skills & topics
- Life Sciences
- AI Model Training
- Data Science
- Machine Learning
- Clinical Analysis
- Predictive Analytics
- Generative AI
- LLM
- Healthcare Data
- Regulatory Affairs
How to get hired
- Tailor your resume: Highlight your graduate degree in life sciences/medical sciences/computer science and 3+ years of experience with AI-enabled life science applications.
- Showcase AI/ML expertise: Emphasize experience with Generative AI, LLMs, and the full software development lifecycle.
- Demonstrate analytical skills: Detail your experience with statistical analysis of large datasets and data preprocessing.
- Communicate effectively: Prepare examples of your ability to bridge technical and non-technical discussions.
- Research Norstella: Understand their mission to improve patient access and their commitment to integrity and learning.
Technical preparation
Behavioral questions
Frequently asked questions
- What is the role of a Senior Life Sciences Analyst, AI Model Training at Norstella?
- The Senior Life Sciences Analyst, AI Model Training at Norstella plays a crucial role in bridging clinical and scientific domain expertise with AI development. You will evaluate and calibrate AI model outputs, ensuring accuracy and clinical reasoning align with specific use cases. Your judgment serves as the key signal to transform capable models into genuinely useful tools for clients.
- What kind of experience is required for the Senior Life Sciences Analyst, AI Model Training position at Norstella?
- Norstella requires a graduate degree in life sciences, medical sciences, computer science, or equivalent professional experience. You'll need at least 3 years of professional experience with production-grade life science datasets and AI-enabled applications, along with experience in statistical analysis of large datasets and data cleaning.
- Does Norstella offer opportunities for growth in AI and Machine Learning?
- Yes, Norstella actively works with advanced technologies like machine learning and predictive analytics. This role specifically involves interfacing with data scientists and machine learning engineers, and experience with Generative AI and LLMs is highly valued, indicating a strong focus on AI/ML development.
- How does Norstella ensure the quality and accuracy of its AI models?
- Norstella emphasizes a human-in-the-loop approach. The Senior Life Sciences Analyst will define and maintain evaluation rubrics, quality criteria, and preference dimensions. They will critically evaluate model outputs, conduct red-team evaluations to find subtle failure modes, and analyze data to guide model improvements, ensuring a high standard of accuracy and clinical reasoning.
- What is the company culture like at Norstella, based on their guiding principles?
- Norstella fosters a culture that is bold, passionate, and mission-first, prioritizing client success and patient access. They value integrity, truth, and reality, encouraging honest feedback and realistic metrics. Kindness, empathy, and grace are key to collaboration, alongside resilience and perseverance in challenging situations, and a commitment to continuous learning through humility and gratitude.
- Are there specific technical skills that are particularly beneficial for this role?
- While a strong life sciences background is essential, experience with Generative AI, especially Large Language Models (LLMs) and agents throughout the software development lifecycle, is a significant advantage. Familiarity with creating and consuming MCPs into Agentic workflows is also a bonus.
- What are the main responsibilities of a Senior Life Sciences Analyst in AI Model Training at Norstella?
- Key responsibilities include evaluating and rating AI model outputs, defining quality criteria for these outputs, acting as an expert-in-the-loop for model commercialization, advising on model behavior interpretation, partnering with data scientists on feedback pipelines, conducting adversarial evaluations, analyzing preference data, and contributing to knowledge graphs and new agentic workflows.
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