Why This Job is Featured on The SaaS Jobs
This Data Scientist II role stands out in the SaaS landscape because it sits at the intersection of product-grade AI and scalable delivery. The emphasis on fine tuning foundation models, working with large datasets, and optimizing training across cloud or distributed systems reflects the direction many SaaS companies are taking as generative and applied ML move from experimentation into core product capabilities.
From a career perspective, the remit builds durable SaaS skills: translating research advances into measurable improvements, designing evaluation and validation practices, and shaping how models are deployed and maintained over time. Exposure to MLOps disciplines like versioning, CI/CD for ML, and production deployment helps bridge the common gap between model development and real customer facing reliability. Experience with modern ecosystems such as Hugging Face and PEFT techniques also transfers well across AI enabled SaaS teams.
The position is best suited to professionals who enjoy hands on model development but also care about operational constraints, reproducibility, and stakeholder communication. It fits someone ready to take ownership of end to end model iteration, and who can collaborate across product and engineering to turn ambiguous use cases into deployed ML systems.
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.
