Why This Job is Featured on The SaaS Jobs
Within the SaaS ecosystem, Data Scientist II roles increasingly sit at the intersection of product differentiation and operational reliability, particularly as generative AI moves from experimentation into customer-facing workflows. This listing stands out for its emphasis on adapting foundation models (LLMs and related architectures) to domain-specific use cases, alongside the practical constraints of running large-scale training and inference on modern GPU/TPU infrastructure.
For a SaaS career path, the combination of model fine-tuning (PEFT methods like LoRA and quantization-aware training) and production-minded practices (experiment tracking, versioning, CI/CD for ML) builds a portfolio that travels well across subscription software businesses. Experience with distributed training frameworks and large data pipelines also maps directly to common SaaS challenges: iteration speed, cost-to-serve, and maintaining model performance as data shifts over time.
This role is best suited to a practitioner who prefers applied work over research-only outcomes, and who is comfortable translating stakeholder needs into measurable model improvements. It will particularly fit someone ready to operate with end-to-end ownership—from data and training through deployment considerations—while collaborating closely with product and engineering partners in a SaaS environment.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Technical Expertise:
- Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc.).
- Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as LoRA, prefix tuning, adapters, and quantization-aware training.
- Proficiency in PyTorch, TensorFlow, Hugging Face ecosystem and good to have distributed training frameworks (e.g., DeepSpeed, PyTorch Lightning, Ray).
- Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI/CD for ML pipelines, and deployment in production environments.
- Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
- Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments.
Applied Problem-Solving:
- Mandatory skill - Demonstrated success in adapting foundation models to domain-specific applications through fine-tuning or transfer learning.
- Mandatory skill - Strong ability to design, evaluate, and improve models using robust validation strategies, bias/fairness checks, and performance optimization techniques.
- Experience in working on applied AI problems across NLP, computer vision, or multimodal systems or any other domain.
Leadership & Collaboration:
- (Preferred) Proven ability to lead and mentor a junior applied scientists and ML engineers, providing technical guidance and fostering innovation.
- Strong cross-functional collaboration skills to work with product, engineering, and business stakeholders to deliver impactful AI solutions.
- Ability to translate cutting-edge research into practical, scalable solutions that meet real-world business needs.
Education & Experience:
- 3+ years of hands-on experience in applied machine learning and data science with Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field or appropriate experience.
- Excellent communication and presentation skills to articulate complex ML concepts to both technical and non-technical audiences.
- Continuous learner with awareness of emerging trends in generative AI, foundation models, and efficient ML techniques.
