Principal Data Scientist

Genentech•South San Francisco, CA

62d•Onsite

About The Position

We're passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world's most complex health challenges and transforming society. Our Data, Analytics, and AI team is dedicated to solving complex healthcare challenges and improving patient outcomes. Data, Analytics, and AI empowers business partners across Commercial, Medical, and Government Affairs (CMG) to make impactful decisions by leveraging data, analytics, business products, and AI/ML to enable fast, targeted actions in rapidly evolving business contexts. Data, Analytics, and AI fosters a unified understanding of customers, actions, and outcomes by integrating analytics and insights seamlessly into CMG's evolving digital, data, and automation platforms, creating scalable solutions and eliminating silos. In Data, Analytics, and AI, you will work as a trusted, objective advisor and expert, recommending critical decisions and actions to be taken with credibility and a focus on driving measurable impact. You will be part of a thriving culture built on collaboration and innovation. The Principal Data Scientist develops and maintains AI-enabled data science products that leverage advanced analytics and machine learning to solve complex business challenges, uncover trends, and enable strategic decision-making. This role combines mathematical expertise, coding proficiency, and innovative problem-solving to create and deploy cutting-edge data science solutions, driving impactful outcomes across the organization.

Requirements

Bachelor's degree in Statistics, Mathematics, Computer Science, or a related quantitative field.
8 years of experience in a data science or a related role.
Strong knowledge of SQL for database management.
Proficiency in programming languages such as Python, R.
Knowledge of SQL for database management.
Solid understanding of statistical methods and machine learning algorithms.
Excellent verbal and written communication skills, with the ability to present complex data analyses to non-technical stakeholders.
Strong critical thinking and problem-solving abilities, with a detail-oriented approach to data analysis.

Nice To Haves

LLM & Gen AI Expertise: At least 4 years of experience implementing LLMs (e.g., GPT, BERT, Claude, etc.) and Generative AI solutions in production environments, specifically for enterprise-level data processing, information extraction, automation, and decision-making, with measurable business outcomes.
Machine Learning & Deep Learning: Strong expertise in Machine Learning and Deep Learning techniques, with a specific focus on NLP-related architectures such as Transformers for text classification, sequence-to-sequence tasks, summarization, and question answering.
Demonstrated working knowledge of recent advancements in LLMs, Agentic Workflow Design Patterns, and open-source frameworks like LangChain, LlamaIndex, LangGraph etc.
In-depth knowledge of Prompt Engineering and Chain-of-Thought Prompting strategies for optimizing LLM-based solutions in production.
Experience working with large, complex data using Hadoop or Spark or any other big data platforms.
Experience with other Data Science and cloud-computing tools and platforms (AWS, GCP, etc.).
Experience with deploying LLMs via third-party API service providers like Open AI, Anthropic, AWS Bedrock etc.
Proficiency using ML in a variety of contexts such as insight generation, ROI calculation, text classification, clustering etc.
Experience with data visualization tools such as tableau, and/or Qlik, and/or data studio etc.
Experience acting as a strategic thought partner to teams; demonstrated ability to solve problems and think outside the box.
Proven track record of leadership, time-management, project management, and teamwork; Strong attention to detail.
Experience translating research or analysis to communicate (in presentations and in writing) concise and compelling business stories that influence decisions and strategy.

Responsibilities

Apply data science and other advanced analytical methodologies, particularly in the areas of Predictive/Generative/Agentic AI using multiple data sources and tools.
Collaborate with data science product owners/managers, data leads, Machine Learning (ML) Engineers, MLOps, and Informatics (IT) team to develop efficient machine learning-based applications, gain alignment, and deliver impactful business insights.
Communicate findings effectively to both technical and non-technical audiences.
Maintain high standards of data quality, security, and governance, ensuring robust documentation and adherence to best practices.
Drive the next wave of development, deployment, and industrialization of Predictive AI, advanced LLM - Generative AI and Agentic AI applications
Proactively identify emerging technologies and champion their integration to address complex Commercial and Medical needs.
Translate deep market, customer, and competitive insights into forwardâlooking AI strategies with senior stakeholders, ensuring solutions not only enhance the integrated customer experience but also anticipate future industry shifts.
Partner with senior leadership to refine and prioritize AI/ML initiatives, ensuring alignment with enterprise objectives. Advocate for dataâdriven decisionâmaking and secure necessary investments in data capabilities.
Oversee complex, largeâscale ML initiatives (including multiâsource data integration and advanced model pipelines) with robust governance, scalability, and compliance frameworks.
Act as a thought leader for applicable data science to elevate the organization's AI maturity by introducing cuttingâedge Data Science methodologies
Ensure cohesive partnerships among Data Science, ML Engineering, MLOps, Product, and Informatics (IT) teams.
Champion dataâcentric culture and influence leadership to adopt forwardâthinking AI solutions enterpriseâwide.
Establish clear metrics of success for all AI/ML programs, hold teams accountable for outcomes, and proactively courseâcorrect when needed. Demonstrate unwavering commitment to highâimpact delivery.
Stay abreast of the latest advancements in data science and AI technologies, applying innovative approaches to enhance product capabilities.
Comply with all laws, regulations, and policies that govern the conduct of Genentech activities.