Principal Data Scientist

Key Skills

Aartificial intelligence, Analytical and Problem solving, AWS, Azure, ChatGPT, Cloud computing, Data science techniques, Deep Learning, Effective communication skills, Generative AI, Google Cloud Platform (GCP), JAX framework, Large Language Models - LLMs, Linux Operating system, Machine learning techniques, Natural Language Processing (NLP), NeurIPS, Pandas, Python Programming, PyTorch, Scikit-learn, SciPy, SQL, TensorFlow, Unix Operating system

Job Description

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

As a data scientist focusing on building and enhancing the Einstein GPT Trust Layer, you will leverage your experience in the areas of fairness, accountability, transparency, and explainability to help develop and refine our generative AI ecosystem, ensuring it meets our stringent data privacy, security, residency, and compliance goals.

We are seeking a candidate proficient in diverse machine learning techniques who can deliver actionable insights. Someone passionate about leading technical teams in building Responsible AI tools. This individual should champion a proactive and informed approach to our AI products, always keeping our customers' needs at the forefront, while embodying our core values of trust, customer success, equality, and innovation.

Responsibilities

• Partner with research and engineering teams to identify and remediate potential bias in data and models, by identifying types of bias for which to test in varying data types and models.
• Make recommendations on approaches for maximizing fairness, tooling needs, and safe thresholds for launch
• Determine and operationalizing trust standards, thresholds, auditing of testing/red-teaming for all Einstein GPT components, apps, and models (e.g. toxicity filter, PII detection)
• Write code for libraries and tools that perform testing and evaluation
• Assist product teams in developing features that deliver model explainability and transparency to our customers
• Collaborate with industry leaders in similar positions in peer organizations on ways to improve the state of responsible AI development

Required Qualifications

• MS or Ph.D. in a quantitative discipline with 5+ years of relevant experience
• Fluent in building/prototyping machine learning models and algorithms and wrangling large datasets
• Experience with LLMs and prompt engineering
• Expertise applying standard machine learning approaches (Regression, Cross-Validation, Boosting, Matrix-Factorization, Decision Trees, Clustering, CNNs, RNNs, Transformers, GANs)
• Proficient in using Python and most common machine learning frameworks (e.g., TensorFlow, Pandas, PyTorch, SciPy, scikit-learn, JAX) to implement models and algorithms
• Proficient in SQL, shell scripting, and Unix/Linux command-line tools
• Grasp of the evolving understanding of fairness and ability to meet both state-of-the-art and global standards for fairness evaluation
• Experience working across teams of engineers, data scientists, and researchers
• Strong communication skills. Comfortable presenting ideas to diverse teams and individuals in multiple formats, from slide decks to informal chats
• Earns trust in relationships both internally and externally, and at all levels of the organization. Challenges the status quo to improve the productivity, effectiveness, and culture of a team - without burning bridges
• Ability to creatively prioritize, stage, and sequence solutions to challenging/complex problems

Preferred Qualifications

• A PhD in CS, Machine Learning, Statistics or relevant field
• 5+ years of industry/applied research experience in machine learning, NLP, deep learning and/or information retrieval
• Track record of industry/applied research experience in in most of the following: Generative AI, Large Language Models (LLMs) applications, NLP, Information Retrieval, Question Answering/Conversational AI
• Expertise with applying LLMs, prompt design, and fine-tuning methods
• Experience designing experiments to evaluate the performance of LLMs and generative AI models
• Experience with red-teaming LLMs
• Strong background in ML approaches and techniques, ranging from building and training deep learning algorithms from scratch to applying Bayesian methods
• Experience with cloud platforms like AWS, Google Cloud, or Azure
• Strong experience leading multi-disciplinary teams driving significant business results
• Top-tier papers published in related areas (e.g. NeurIPS, FAccT, AIES, or similar conferences)

Salesforce welcomes all.

Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.

For California-based roles, the base salary hiring range for this position is $188,200 to $305,600.

Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, benefits. More details about our company benefits can be found at the following link: https://www.salesforcebenefits.com.