Principal Data Scientist

Title:  Principal Data Science 
Location: Remote 
Clearance: Public Trust Eligibility

Who We Are:

Rackner is a fast-growing software consultancy focused on building cloud-native solutions for startups, enterprises, and the public sector. We are passionate about solving big problems through innovation, specializing in end-to-end application development, DevSecOps, AI/ML, and systems architecture. We take a cloud-first, cost-effective approach to innovation, serving a diverse and expanding list of industries.

Position Overview:

Rackner is seeking a Principal Data Science to architect advanced machine learning solutions and lead the strategic implementation of cutting-edge AI technologies, with a focus on Artificial Intelligence (AI) and Large Language Models (LLMs).
You will support the Food and Drug Administration (FDA) — an organization vital to protecting public health — and work at the forefront of regulatory science and technology.

This leadership role combines deep technical expertise with strategic vision, allowing you to drive innovation, mentor team members, and shape how forward-thinking organizations approach their most critical data science challenges.

Key Responsibilities:

• Architect and develop AI/ML models for analyzing regulatory documents
• Collaborate with FDA subject matter experts to validate models and ensure relevance for regulatory decision-making
• Implement data preprocessing and feature engineering pipelines for unstructured data
• Optimize model performance with a focus on accuracy, efficiency, and scalability
• Ensure compliance with FDA Good Machine Learning Practices (GMLP) and regulatory requirements
• Conduct predictive modeling, optimization, and continuous model monitoring
• Deliver client-facing presentations to executive stakeholders
• Identify new opportunities for innovation and strategic AI/ML initiatives
• Lead initiatives focused on LLM development, including fine-tuning, evaluation, and deployment strategies

Qualifications:

• Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field
• 7–8 years of professional experience in data science or analytics, with leadership exposure
• 2–3 years of hands-on experience with LLMs (e.g., fine-tuning, prompt engineering, instruction tuning)
• Ability to obtain a Public Trust Clearance (required)
• Authorization to work in the United States

Technical Proficiency:

• Strong proficiency in Python (preferred) and experience with other languages such as C, R, Java, or Scala
• Expertise in statistical modeling, machine learning, NLP, and deep learning techniques
• Familiarity with AWS services: Athena, S3, Glue, SageMaker, Comprehend, Bedrock
• Preferred: Exposure to MLOps practices, big data technologies (Hadoop, Spark), and cloud platforms

LLM-Focused Skills (Preferred, but not all required):

• PEFT (e.g., LoRA/QLoRA) for efficient fine-tuning
• Instruction fine-tuning, Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT) or Tree-of-Thought (ToT) prompting
• Quantization, pruning, and knowledge distillation techniques
• Experience with Hugging Face Transformers, LangChain, Llama Index, or large-scale training frameworks
• Familiarity with LLM evaluation metrics, model interpretability, and optimization best practices

Soft Skills:

• Exceptional written and verbal communication skills
• Strong problem-solving abilities and passion for continuous learning
• Collaborative, team-oriented mindset with the ability to partner with diverse stakeholders

Additional Information / Benefits:

Rackner invests in employee development and success. We proudly offer:

• 401(k) with 100% company match up to 6%
• Highly competitive Paid Time Off (PTO)
• Comprehensive health insurance (Medical, Dental, Vision) with a broad provider network
• Life Insurance and Short- & Long-Term Disability coverage
• Industry-leading weekly pay schedule
• Home office and equipment reimbursement plan
• Fitness/Gym membership eligibility
• Employee swag, snacks, and company events